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Rationality and intertemporal choice
Adam Gifford, Jr.
Abstract The alleged problems associated with self-control, hyperbolic discounting and other
examples of seemingly irrational intertemporal choice are examined in the context of an
evolution-based neurobiological model that emphasizes the role of the biological evolution of big
brains and language and the cultural evolution of institutions. There is no utility function in the
brain; it has no central-planner, in fact, the brain is a self-organized complex system, a
decentralized spontaneous order. This spontaneous order is coordinated, much like an economy,
by a distributed network that maintains and makes available the discounted net value of various
options to decentralized and specialized areas in the brain when making decisions. Further, that
decision making is embodied and embedded in the decision making environment. For humans,
an important part of that environment is the social environment consisting of institutions and
other components of culture. It was, in part, the evolution of this environment that made long-
range planning possible. Additionally, it is very often the lack of embedded experience with the
environment that leads to what seems to be irrational intertemporal choices. In fact, under close
examination the evidence for consistent irrational intertemporal choice is weak.
Keywords Self-control, Discounting, Hyperbolic discounting, Learning
JEL classification A12 D81 D87 D91
_________________A. Gifford, Jr.
Department of Economics, California State University, Northridge, CA 91330-8374, USAe-mail: [email protected]
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1 Introduction
Procrastination and other self-control problems seem to be common features of our existence.
Estimates indicate the 80%-95% of college students engage in procrastination, approximately
75% consider themselves procrastinators, and almost 50% procrastinate consistently and
problematically. [P]rocrastination is also widespread in the general population affecting some
15%-20% of adults (Steel 2007, p. 65). The big drop in the percentage of procrastinators in
general population of adults compared to college students, is not surprising given that the
prefrontal cortex which plays an important role in intertemporal choice is not fully developed
until an individual reaches his/her early twenties. Importantly, does a procrastination rate of
15%-20% mean it is widespread among adults? Further, what do self-reported levels of
procrastination really imply? This paper will address these questions and other aspects of
seemingly irrational intertemporal choice in the context of an evolution-based neurobiological
model that emphasizes the role of the biological evolution of big brains and language and the
cultural evolution of institutions.
Bayes rule given by Equation (1) will be used to provide the framework for the analysis
of intertemporal choice:
P(H|e) = P(H)P(e|H)/P(e) (1)
For our purposes it will be useful to recast Bayes rule as posterior odds, since [t]he essence of
Bayess rule is conveniently portrayed using odds andlikelihood ratio parameters Pearl (2000,
p. 6).
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O(H|e) = O(H)L(e|H) (2)
Thus, Bayess rule dictates that the overall strength of belief in a hypothesisH
, based on both
our previous knowledge and the observed evidence, e, should be the product of two factors: the
prior odds O(H) and the likelihood ratioL(e|H). The first factor measures thepredictive or
prospective support accordedHby the background knowledge alone, while the second represents
the diagnostic orretrospective support given toHby the evidence actually observed (Pearl
2000, p. 7). For the purposes of examining intertemporal choice (2) can be interpreted as:
O(2B|~h) = O(2B)L(~h| 2B) (3)
Where the term on the left side of Equation 3 gives the conditional odds that by forsaking a
currently available option h, a bird in the hand, and spending additional time, energy, and other
resources, that 2B, two birds in the bush, can be acquired.
Depicting intertemproal choice as essentially probabilistic puts emphasis the uncertain
nature of the problem. After all, there may not be two birds in the bush or someone else may get
them before you do or the birds may fly away. Furthermore, an important component of
discount rates is a product of the uncertainty that is associated with all delay. Hayden and Platt
find that in Rhesus monkeys, and very likely in humans, the decisions involving risk and those
involving delays share the some neural mechanisms. [U]ncertainty about when a reward might
be realized and uncertainty about how much reward might be realized are naturally related. One
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common explanation1 for the generality of temporal discounting is that delayed rewards might be
viewed as risky, thus leading to a preference for the sooner option in intertemporal tasks (Platt
and Huettel 2008, p. 399). In nature, the acquisition of rewards plays out over time and the risk
that a reward may not be available is not much different than the risk that it will be available but
with a potentially long and uncertain delay. A bird in the hand is not only available now, there is
also not much risk associated with it. Given the high degree of uncertainty in our evolutionary
environment, in which resources were being drawn from a common pool, taking now rather than
later was most often the optimal strategy. Interestingly, Stephens et al. (2002) report studies with
animals showing that a one-second delay of a food reward reduces the value of the reward by as
much as 50%, a discount rate so large that it can only be a product of perceived uncertainty.
Also consistent with the notion of uncertainty-driven discounting among humans, in field
experiments in Denmark, Harrison et al. (2002) find an average discount rate of 28% and suggest
this high rate may result from the fact that, despite our extensive attempts to encourage
credibility, the subjects might have doubted that we would actually follow through on the
payments. [and thus they viewed the payments] as risky prospects (Harrison et al. 2002, p.
1613).
As we will see, Bayesian-based decision making is common in the animal kingdom, but
human intertemporal decision making is fundamentally different from that of other animals in a
least three ways: 1) Humans can make plans about a distant future, involving years or decades,
but for most other animals the future consists of a few seconds or minutes away, and in only a
few rare cases does it extend even to hours. So for humans alone the temporal gap between ~h
and2B in planning can be quite large. The human ability to visualize and make plans for the
1 See McNamara and Houston (1986).
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distant future is sometimes called mental time travel. 2) It is not enough to have the cognitive
capacity to make plans involving the distant future; for the plans to be viable there must be
reasonable odds that the current sacrifice will pay offit must be likely that giving up ~h today
will yield2B in the future. As we will see, the evolution of large brains and language made
possible a symbolic capacity in humans that fundamentally changedO(2B)andL(~h| 2B),
resulting in a significant increase in O(2B|~h), and as a result the evolutionary viability of long
term planning. 3) The essential cognitive decision-making action, including the Bayesian
calculation of odds, takes place at the subpersonal levelbelow the level of consciousness.
2 But are we Good Bayesian decision makers?
A potential problem with assuming that a Bayesian-based approach to intertemporal choice is
used by decision makers is that, though Bayes rule is a fundamental component of normative
economic models of rational choice, one of the most seemingly incontrovertible experimental
results is that individuals are very bad at applying the rule. Stanovich and West (2000)
correlated performance on various reasoning tasks that result in decision anomalies in the
experimental literature with SAT scores, and they found that generally higher SAT scores were
positively correlated with decision makers who more closely conformed to the norms of
rationality, except in the case of Bayesian inference where they found that higher SAT scores
were correlated with poorer results. Furthermore, even Ph.D.s are regularly stumped by some
problems requiring Bayesian reasoning (Morgan et al. 1991). A closer look, however, suggests
that outside the experimental lab we may not be such bad Bayesians after all.
Choice involves perception, cognition and action, not as separate processes, but rather as
interacting and ongoing processes. In fact, agents learn about their environment, and thereby
form their expectations, through on-going sensory-motor interaction with that environment, and
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in the process the agents behavioral performance is improved. This is a process called
embodied and imbedded cognitionour cognition is a product of our brain/bodies active
interaction in its environment. The human environment is to a large extent social and consists of
the institutions, culture, and other social constructs that are made possible by our large brains and
symbolic language capacity. Choices in the lab are very often disembedded, divorced form the
natural decision-making environment. In fact, not uncommonly in more natural settings,
optimality models produce accurate predictions of behavior (see Todorov 2004 for a review).
The success of optimality models in predicting behavior is a product of the fact that [e]ven if
skilled performance on a certain task is not exactly optimal, but it is just good enough, it has
been made good enough by process whose limit is optimality (Todorov 2004, p. 907).
Importantly, our perception and other cognitive systems employ Bayesian and other
optimal strategies, to overcome the fact that perception and cognition are ill-posed problems. In
perception, for example, the two-dimensional data projected onto the retina provide insufficient
information by themselves to construct the three-dimensional reality that we perceive. When a
point in space is projected onto the retina, the corresponding point in the retinal projection could
have been generated by an infinite number of different locations in the physical world.
Similarly, an array of points in the retinal image could have arisen from an infinite number of
physical configurations. Therefore, the relationship between any projected image and its source
is inherently ambiguous. Nevertheless, the distribution of the distances of unoccluded [visible]
object surfaces from the observer and their spatial relationships in normal viewing must have a
potentially informative statistical structure. Given this inevitable ambiguity in vision, it seems
likely that visual systems have evolved to take advantage of such statistical structure, or
probabilistic information, in generating perceptions of physical space. Any probabilistic strategy
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of this sort can be formalized in terms of Bayesian optimal observer theory (Yang and Purves
2003, p. 632).
The underdetermined nature of perception and cognition suggests that these processes are
statistical problems, and, increasingly, neuroscientists and cognitive scientists are finding that
using a Bayesian approach to perception, cognition and action produces remarkable successes
in generating models that predict existing empirical findings. (Shiffrin 2003, p. 342).2 There
is a significant amount of evidence that supports the view that the brain employs Bayesian
strategies at close to optimum levels in the processes of perception, cognition and action, but that
most of the action takes place at the subpersonal, nonconscious level. Individuals tend to be bad
Bayesians when they attempt to solve problems that require the conscious application of Bayes
rule, but our subpersonal cognitive circuits successfully employ Bayes rule in our day-to-day
decision making.
Using current perceptions and memory of past experiences, in the process of planning the
brain simulates alternative courses of action.3
Gilbert and Wilson call this ability affective
forecasting or prospection (Gilbert and Wilson 2007, Gilbert 2005, Wilson and Gilbert 2005). It
is affective in the sense that the agent is trying to determine which course of action will lead to
the greatest happiness and because affective systems in the brain including the insula, amygdala,
ventral prefrontal cortex, and the midbrain dopamine system of the basal ganglia are all activated
during the prospection process. Along with the affective systems there are several other
components of the brains mechanisms of choice, including the dorsolateral prefrontal,
2 Also see, Doya (2008), Geisler and Diehl, (2003), Glimcher, (2003), Sharma et al. (2003).3 See Gilbert and Wilson (2007), Jeffery (2004), Hurley (2008), Szpunar et al. (2007) and Wilson and Gilbert(2005).
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cingulate, and posterior parietal cortices (see Platt and Huettel 2008), as well as the mirror
neuron systems described below.4
Several features of the mechanisms of choice are important in what follows, including
that it is a fundamental mistake to start with the notion that there is a utility function in the brain.
This error is the result of attributing the characteristics of human mental constructions (such
as logic and mathematics) to human reasoning [itself]... (Edelman 1992, p. 228). There are, in
fact, several problems with the idea of a utility function in the brain. One is that it would lead to
regress. Additionally, a utility function in the brain is a central planning view of cognition and
would result in the same sort of Hayekian knowledge problem that occurs with a centrally
planned economy. Other factors to keep in mind are that introspection provides no useful data
about our decision-making mechanisms and that the decision-making processes are learning
mechanisms.
3 The coordination of decentralized knowledge
In The Sensory Order(1952), Friedrich Hayek argued that the mind/brain was a spontaneous
order.5
Hayeks approach is consistent with the notion that the brain has no central-planner
there is no ghost in the machine, no utility function in the machine. The proposal that the brain
is a complex system functioning as a decentralized spontaneous order is shared today by many
neuroscientists; see, for example, Calvin (1996), Dennett (1991, 1996), Edelman (1992),
Edelman and Tononi (2000), Fuster (1995), Franklin (1996), Koch and Crick (2001), and Koch
(2004).
4 Neu (2008) presents a schematic diagram (p. 173) of these brain mechanisms which incorporate perception andmemory in formation of plans for the future.5 Discussions of The Sensory Order from an economic perspective can be found in Caldwell (2004), who provides agood overview of the Hayeks goals in writing the book and in McQuade and Butos (2005) who focus on the role ofthe brain as a spontaneous classifier of information from the environment and also relate that function to otherspontaneous ordering structures such as the market.
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Taking the mind/brain as a decentralized spontaneous order leads logically to the
question: What mechanisms spontaneously coordinate the specialized areas in the brain that
contain decentralized specialized knowledge (similar to the function of prices and markets in an
economy)? Neuroscientist Edmond Rolls discusses the workings of some of the neural
coordination mechanisms. He considers that the [o]peration of the brain to evaluate rewards
and punishers is the fundamental solution of the brain to interfacing sensory systems to action
selection and execution systems. Computing the reward and punishment value of sensory
stimuli, and then using selection between different rewards and avoidance of punishments in a
common reward-based currency, appears to be the fundamental design that brains use in order to
produce appropriate behavior ( [emphasis added] Rolls 1999, p. 5). Various components of the
brains emotional systems assign value, maintain emotional and value memory, adjust marginal
value in response to current consumption, and change associated value as a result of the adaptive
learning that results from the bodys interaction with the environmentall processes that
facilitate the ordering of the decentralized system.6
We learn how various activities and goods
satisfy basic goals, and in the process value is attached to those activities and goods.
Furthermore, the values attached to goods behave like marginal values in that, in a given period
of time, they decline with consumption (see Gottfried et al. 2003, Tremblay and Schultz 1999
and Rolls 1999). Since decisions involve uncertainty and delays, the common currency is
adjusted to reflect uncertainty and discounted to reflect the delay associated with securing a good
6 A biological model of this process was developed in Montague, Dayan, Person and Sejnowski, (1995), andMontague, Dayan and Sejnowski, (1996), and a survey of this work is presented in Schultz, Dayan, and Montague,(1997). A neural network model was developed and [t]here is good evidence for similar predictive responses inprimate... [learning] systems (Montague et al. 1996, p. 728). This research examines the attachment of value viaareas of the brain involved in motivation and goal-directed behavior. They model neurons involved in thetransmission of information between the areas of the basal ganglia and cortex[m]ultiple lines of evidence supportthe idea that these neurons construct and distribute information about rewarding events (Schultz et al. 1997, p.1594).
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as well as the direct costs of each option.7 We will refer to these expected net values used in
decision making as decision values. Finally, the emotional systems that assign value also
motivate action, where the motive force generated by the system reflects the expected net benefit,
about which more will be said below (see Gifford 2005 for a more detailed discussion of these
issues).
All decisions are about the future, but humans alone can decide things involving an
extended future. In fact, Daniel Gilbert suggests that [t]he greatest achievement of the human
brain is its ability to imagine objects and episodes that do not exist in the realm of the real, and it
is this ability that allows us to think about the future. the human brain is an anticipation
machine, and making the future is the most important thing it does (Gilbert 2005, p. 5). The
ability to imagine an extended future, called mental time travel by Suddendorf and Corballis
(2007), seems to be a trait unique to humans, other animals perceive and make decision about
only a local future.8
7 Actually, many other mechanisms and systems are involved in coordinating the system. The binding problem inperception, for example, arises from the brains architecture, in which the outside world is represented by nervousactivity in a hundred or more distinct regions. When looking at a person, the colors of the clothes, hair and skin, aswell as movement, sound and the face are all processed in separate areas. Nevertheless, all of this disparate activityis experienced as a single integrated percept Koch (2004, p. 167-168). The synchronized firing of neurons in theso called gamma range (30-80 Hz) is thought to play a role in transient, long-range coordination of distinct brainregions. Canolty et al. ( 2006, p. 1626) Higher level coordination across several of these regions, also includingregions coordinated by high gamma oscillations (80-150 Hz), is facilitated by low frequency theta oscillations (4-8Hz), so that there seems to be a hierarchy of coordination with the low frequency theta oscillations coordinatingdifferent groups of neurons in regions each coordinated by higher frequency oscillations (Canolty et al. 2006).
Energy homeostasis not only uses the reward systems mentioned above, but many other systems as well toregulate food intake and body weight. To name just a few, the level of the circulating hormone leptin released byadipose fat provides a long-run feedback feeding signal to the brain reflecting the level of long-term energy storage.
Levels of circulating insulin and glucose provide short-term energy feedback signals. Hormones released by the gut,Peptide YY and ghrelin respectively, signal satiety and promote feeding (see Alemseged et al. 2006, for a review).The fact that leptin, insulin, glucose, Peptide YY, ghrelin and other information bearing price-like signals thatoriginate outside the central nervous system together with those signal bearing molecules strictly endogenous to thebrain all interact to facilitate energy homeostasis, illustrates the important point that coordination of the systeminvolves an interaction of both brain and body.8 Recent research has shown that western scrub-jays (Raby et al. 2007) and apes (Osvath and Osvath 2008) flexiblyplan for the future. The apes saved a tool that would allow them access to highly preferred fruit drink in the future.The jays cached pine nuts in a room that they had been placed in previously without food, facilitating futureavailability. In both cases the animals sacrificed current rewards to provide for future reward access. Though the
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Interacting with the world, including the social world is facilitated by neurons called
mirror neurons, found in both monkeys and humans, that fire when an individual performs an
action and when the individual observes the same action being performed by another (see,
Gallese et al 1996 and Rizzolatti et al 1996). Another set of neurons, called canonical neurons,
fires when a monkey performs a particular manual action to grab an object and also when the
monkey merely observes the object. Both sets of neurons, as well as other motor neurons,
facilitate learning about the world through direct and indirect interaction with that world:
learning by doing and by social learning from others; the brain then uses that information in
decision making. These neurons are active in the simulations of actions, which facilitates
predictions of the possible outcomes of various actions and estimates of the costs of those actions
and outcomes (see Gallese 2001 and Calvo-Merino et al. 2005). We acquire information about
the expected costs and benefits of possible actions through interaction with the world; our brains
then use simulations that incorporate the discounted expected costs and benefits of the actions
when generating new plans of action. After an action the expected costs and benefits are subject
to revision based on the outcome of the action, making choice an iterative, on-going learning
process (Doya 2008). Importantly for our purposes, these systems operate below the level of
consciousness; the motor simulations are implicit, automatic, andnonconscious (Gallese
2001, p. 41).
The brain runs simulations of the alternative courses of action being considered, for each
specific action, the simulation is used to estimate the direct costs of that action, including energy
and time costs. Also associated with each action is its discounted expected value, which is the
conditional probability from Equation 1 multiplied by the common currency or reward value
future time frame in both cases was short, 70 minutes for the apes and overnight for the jays and the focus of theplanning was a primary reinforcer.
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mentioned above. Finally, associated with each option is a measure of volatility, or the variance
associated with the expected reward value (Behrens et al. 2007). The simulations allow for the
comparison of the direct cost, expected value and variance associated with each option when
choosing among them.
Neural mechanisms that simulate possible futures during decision making commonly
incorporate a mechanism that allows the evidence for each alternative being considered in a
particular choice situation to accumulate toward a threshold level. As a decision maker
deliberates over various available alternatives, for example, choosing a restaurant by considering
characteristics such as distance, price, cuisine, quality and ambiance of each alternative, an
accumulator for each option builds up a value reflecting the weights assigned to each
characteristic of each alternative until one of the options reaches a threshold level and that
alternative is then selected.9 Different decisions will use different accumulator threshold
levelsthat is, different levels of total evidence required to reach a decision, depending on
factors such as importance of the outcome (e.g., which tie to wear would typically have a lower
threshold then which job to take) and the time available to make the decision. A variable
threshold reflects the trade off between decision making time and the amount of evidence
deemed necessary to reach a given decision. Moreover, evidence in many decisions will come
not just from memory but from advice from others, for example, from restaurant reviews and
many other forms of research.
During the deliberation/planning, the decision maker considers alternative plans of action
and picks a winning plan using a decision-rule based on the accumulation threshold. This
winning plan will have the highest discounted net expected value, holding variance constant.
The net expected value reflects the odds in Equation 3, O(2B) that arebased on prior knowledge
9 See Churchland et al. (2008).
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and learning alonethe support accorded the hypothesisby the agents background
knowledge. When the plan of action is completed and the good is actually consumed, an actual
net value will register. A prediction error is then determined by subtracting the expected net
benefit from the actual net benefit; the result of this process is considered better than expected if
the prediction error is positive, as expected if zero or worse then expected if negative. This
learning system is sometimes called the actor-critic model of reinforcement learning, where the
prediction error plays the role of critic (Chiu et al. 2008, p. 517). The prediction error signal, in
part, determinesL(~h| 2B) the likelihood term from Equation 3, which reflects the diagnostic or
retrospective support given to hypothesisby the evidence actually observed (Pearl 2000, p. 7).
This evidence represents crucial direct feedback that plays a key role in the formation of the
agents future expectations. A positive prediction error better than expectedincreases the
probability that the agent will take that action in the future, whereas a negative prediction error
decreases the probability that the agent will take the action in the future. The extent of
adjustment to the most recent prediction error experience is determined by the learning rate,
which reflects the animals current level of understanding of the environment (Rushworth et al.
2008, p. 389). A high learning rate means that the agents future behavior changes significantly
in response to the most recent experience, a low learning rate places more emphasis on prior
experience and consequently results in a smaller behavioral change in response to the most
recent experience.10
Behrens et al. (2007) present evidence that humans can track the statistics
of a reward environment and adapt their learning rate accordingly, finding that the anterior
cingulate cortex plays a key role in this process (Behrens et al. 2007 p. 1214). Further, this
learning rate modulation is predicted quantitatively by a Bayesian learner carrying out the same
task (Behrens et al. 2007, p. 1214). The volatility of the environment is key in the
10 This approach to human interaction with the environment and decision making is similar to that of Heiner (1983).
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determination of the learning rate, volatility determines how much weight we place on past
experience and the degree to which we expect the future to be markedly different. A highly
volatile environment will result in a high learning weight. Volatility, or variance, plays two roles
in decision making: during the simulation process the winning option will be selected through a
process that involves an optimal trade-off between mean and variance. And, once an option is
selected and consumed, volatility will determine by how much the new information provided by
the most recent experience affects future behavior.
There is another learning signal that can affect the agents future behavior. This is
sometimes called the fictive learning signal, and it reflects the reward that would have resulted if
the agent had selected an alternative course of action (see, Chiu et al. 2008 and Lohrenz et al.
2007). From rewards [actually] gained to rewards that that might have been, actual
experience and fictive outcomes generate complimentary learning signals that guide everyday
behavior (Chiu et al. 2008, p. 517). When [the] hypothetical payoffs, or fictive rewards, differ
from the rewards expected from the current value functions, the resulting error signalscalled
fictive reward prediction error, or regretcan be used to update value functions of
corresponding actions (Lee 2008, p. 405-406). The fictive error signalthe difference between
the reward from the next best alternative not taken and the actual reward receivedfacilitates the
updating of the net expected values of options not taken when the agent made the original choice
and also the relative comparison of values across options. When the fictive error is positive the
agent feels regret. Regret is a common symptom of problems of self-control. The fictive error
signal in living organisms may then be considered the output of an endogenous supervisor
that modulates or complements the error signal coming to the actor from the [prediction error]
critic (Chiu et al. 2008, p. 517).
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In experimental settings the fictive error signal is often studied using gambling or
prisoners dilemma games (see Chiu et al. 2008 and Marchiori and Warglien 2008 respectively).
In these experiments, the subjects are immediately aware of the reward they would have
receivedthe fictive rewardif an alternative option had been selected instead of the choice
they actually made. Outside the laboratory setting we very often do not directly observe what
might have been, but instead must use our imaginations to try to determine what rewards were
forsaken by becoming, for example, an economist instead of an accountant. Volatility will also
determine the value and consequent adjustment in future behavior that results from the fictive
error signal, and these fictive reward comparisons will involve their own learning rate.
The neural model of choice considered here facilitates decision making involving
potential outcomes that have not been personally experienced and that may not actually be
experienced for a considerable time period, such as saving for retirement. In these circumstances
the agent will be acting without actual feedback from past experience from either prediction error
or fictive error signals. These situations force the agent to rely on secondary background
knowledge alone, part of what is normally reflected in O(2B) without the benefit of past personal
evidence actually observed, that is, pastL(~h| 2B). It is likely that the decision variables
expected value, expected direct costs and expected varianceused will be less accurate than
those used when making choices between options with which the agent has had direct personal
experience and feedback. Our large brains, language and culture allow us to make decisions
between alternatives only imagined, without the benefit of direct past feedback, so the prediction
error and the fictive errors experienced in the future when the good is actually consumed can
potentially be large. This suggests that the problems associated with never-before experienced
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options redeemable in the distant future is just as likely to be lack of information as lack of self-
control.
Learning rates will be lower in a more stable, less volatile, environment, for humans,
perhaps the most volatile component of their evolutionary environment was the social
environment. Volatility explains why how far we look back in time, determines how far we look
forward. By reducing volatility, human culture and institutions facilitate planning for a more
distant future. The past will tell us more about the future when the environment is more stable.
In fact, remembering the past and simulating a future use overlapping cognitive mechanisms (see
Schacter et al. 2008 and Szpunar et al. 2007), and it seems that the fact that humans can time-
travel far into the future is a product of being able to travel back far into the past. This suggests
that low learning rates and prospection are tied, at least in part, to an evolutionary process that
increased environmental stability. This stability is likely to have been the result of the evolution
of institutions made possible by a process of gene-culture coevolution (see Richerson and Boyd
2005).
4 Social volatility and complexity
The large brains and language that made prospection and lower learning rates possible
are a product of the evolutionary pressures of living in social groups, because social animals
must develop complex forms of social knowledge to predict the behavior of other members of
their social group, manipulate that behavior and ultimately foster complex cooperation. The
latter is an idea that is often referred to as the Machiavellian intelligence hypothesis (MIH)
(Byrne and Whiten 1988, Whiten and Byrne 1997).
Flinn et al. sum up the MIH: in considering the evolution of our hominin ancestors
mental capacity, it seems that [t]he primary mental chess gamewas with other intelligent
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hominin competitors and cooperators, not with fruits, tools, prey, or snow. Human social
relationships are complex andvariable. Predicting future moves of a social competitor-
cooperator, and appropriate countermoves, amplified by networks of multiple relationships,
shifting coalitions, and deception, make social success a difficult undertaking ([emphasis added]
Flinn et al. 2005 p. 13). Humans are the most social and most cooperative of the primates, and
the MIH suggests that our large brains evolved to reduce the volatility of living in complex social
groups.
Given all this, it seems thatHomo sapiens cognitive capacity evolved as a result of an
evolutionary arms race to deal with the problems of politics, not economics; that is, it evolved
not directly as a result of the problems of making a living. In order for our human ancestors to
take full advantage of the various gains from cooperation, they had to reduce the volatility of
social living, in part, by reducing the tendency of the players of the social game to cheat. A
product of this evolutionary process is that humans have certain evolved mental capacities that
other animals lack. This evolved cognitive capacity facilitates earning a living by making
possible: complex culture, the ongoing evolution of that culture, and importantly, a level of
cooperation not seen in non-human contexts. In fact, as we will see, institutions and other
aspects of culture themselves enhance our cognition.
Culture makes us smart not only because cultural evolution has over time greatly
expanded our stock of knowledge, but also because culture changes the way we think (see
Dennett 1996, Donald 1991, Tomasello 1999, Tomasello 2003 and Tomasello et al. 2005). One
reason that this is the case is that culture consists of social habits that, like private habits,
conserve on costly cognition. Not only do institutions and culture increase stability, they make
human cognition possible. The next section explores how this was possible.
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5 Cognition, in tentionality and social facts11
Perhaps the most important contribution to social stability and cognition is the human
ability to create what philosopher John Searle calls social facts. Searle breaks down the world
into two types of facts, brute facts and social facts. [M]ountains and molecules exist
independently of our representations of them. However, when we begin to specify further
features of the world we discover that there is a distinction between those features that we might
call intrinsic to nature and those features that exist relative to the intentionality12 of observers,
users, etc. (Searle 1995, p. 9). Social facts are [o]bserver-relative features [of the world that]
exist only relative to the attitudes of observers ([emphasis added] Searle 1995, p. 11).
Mountains and molecules are brute facts that exist independently of our attitudes toward them,
whereas institutions and other collective social facts exist only because of the collective
intentionality of the users of the institutions. Searle argues that institutions are built from or
upon institutional or constitutive rules, and that without the rules the institutions do not exist.
Consider a simple institution, the game of chess. The rules are constitutive of chess in the sense
that playing chess is constituted in part by acting in accord with the rules. If you dont follow at
least a large subset of the rules, you are not playing chess (Searle 1995, p. 28). Social facts can
be grounded by physical things, such as a chess board, which even with its specific physical
characteristics is only a chess board because of collective intentionality.
Language allows humans to create a form of shared collective intentionality that is
symbolic in nature. This shared collective intentionality is a form of shared mental human
capital, or a shared mental public good. Our symbolic ability and the ability to create shared
intentionality allows humans to create objects, classifications and boundaries, something that is
11 Parts of this section were taken from Gifford (1999) and Gifford (2005).12 Intentionality is used here in the philosophical sense that our perceptions and thoughts are about something.
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not possible for other animals. These abilities allow us to create new goods and public goods
and institutions that are essentially joint mental constructs, and we interact and use these goods
as aids to cognition in ways that amplify many times the advantages we have over our next
closest rivals in cognitive capacity.13 These abilities allow us to segment and classify our world
abstractly, symbolically, and into the extended future. Our symbolic ability allows us to create
such mental constructs as promises, property rights, institutions, obligations, restaurants, stocks
and bonds, commitments, contracts, marriages, property, money, elections, governments,
presidents, corporations, universities, and football games (Searle 1995, p. 97). Significantly,
a system of collectively recognized rights, responsibilities, duties, obligations, and powers
added ontoand in the end was able to substitute forbrute physical possession and
cohabitation [allows for] a much more stable system of expectations (Searle 1995, p. 81).
These institutional arrangements allow for the formation of long-term expectations and
constraints that reduce the learning rate and lower the cognitive cost of making long-term plans.
In his bookThe Construction of Social Reality (1995), Searle argues that the symbolic and
linguistic facility ofHomo sapiens allows us to mentally construct our social reality, and the
capacity to understand our socially constructed reality rests on the ability to internalize the
statistical regularities in our environment, including our shared social mental environment.
Language is the substrate of human culture and institutions; it allows humans to mentally
construct the components of our culture. An important social fact made possible by human
evolution is the creation of a system of property rights that, as emphasized by Searle (1995, p.
13 Here Searle, is not defining institutions at the level of , for example, the new institutional economics, but ratherhe is examining the human cognitive capabilities that make those institutions possible. Searle shows that the humancapacity to create institutions relies on the ability to share intentionality and to think symbolically. Searle is lookingat institutions at a lower level than is normally done by economists, he is looking at the cognitive mechanisms thatmake institutions possible, whereas, economists examine the higher level structural relationships that make in up ourinstitutions, such as laws, contracts, governments and codes of ethics, for example, see Landa (1999).
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81), replaced brute physical possession and cohabitation [facilitating a much more] stable
system of expectations.
Our large brain, language and culture evolved to facilitate increased cooperation in the
complex, volatile human social environment. By increasing social stability this process
facilitated a reduction in the learning rate and enabling the evolution of cognitive mechanisms
that facilitated planning into a more distant future. The planning process makes use of the
culture that is embedded in our brains that the individual has acquired over time, important
components of which are social facts. Social facts and culture facilitate cognition, in part, by
becoming embedded in our brains as background knowledge, O(2B). Furthermore, this
knowledge and other components of our cognitive process operate at a subpersonal,
nonconscious level.
6 The nonconscious simulations of possible futures
The simulation systems use fast decentralized parallel processing, unlike conscious
processing, which is a serial mechanism with serious capacity limitations. These disaggregated
mechanisms use parallel processes to overcome the knowledge bottleneck that would confront a
centrally planned system, because the mechanisms facilitate processing of the multiple options
and their characteristics simultaneously. To take a simple examplematching a name to a
facethe mental search process does not involve seeking a match [by] sorting through
possibilities one at a time, but rather involves a global search where several possibilities are
considered simultaneously, with the best match being retrieved (Hills 2006, p. 24). In conscious
deliberation during decision-making, several nonconscious simulations take place at the same
time, where the results of the simulations are compared using the decision values to determine a
winner, which then (as when trying to match a name to a face) pops into consciousness.
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Consistent with Hayeks argument in The Sensory Order, the brain/body is a self-
organized complex system. The brain/body is a complex self-steering system14, but it is one
without a conscious or even nonconscious steerer. Neil Levy, addressing this issue, asks when
consciously deliberating[w]hat role does consciousness actually play? What is really
happening, when you consciously weigh reasons? Each reason, in favor of or against a course of
action, has a weight independent of your deliberation. Where does this weight come from? It
seems that it is assigned unconsciously, or at least independently of consciousness (Levy 2005,
p. 72). Levy goes on to say that [w]e cannot control our decision-making, for a simple reason.
It is this: decision-making is, or is an element of our control system, whereby we control our
activity and thereby attempt to control our surroundings. If we were able to control our control
system, we should require another, higher-order control system whereby to exert control. And if
we had such a higher-order control system, the same problems would simply arise with regard to
it. The demand that we exercise conscious will seems to be the demand that we control our
controlling. And that demand cannot be fulfilled (Levy 2005, p. 73). This argument rules out
conscious steering; the decision values used in our decision making are provided by the
nonconscious mechanisms mentioned above. Wilson (2002) argues that individuals have
conscious access to a great deal of information about their current memories, thoughts and
perceptions, but they are not conscious of the decision-making processes. We are conscious of
mental contents, not mentalprocesses Wilson (2002, p. 105). In fact, the human brain is a
remarkable decision-making machine, but it was not designed to provide direct access to its inner
processes and workings.
The experimental findings of Benjamin Libet and colleagues reveal the nonconscious
nature of decisions. In 1965 Kornhuber and Deecke demonstrated, using surface measurement
14 See van Duijn and Bem (2005).
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of activity in the motor cortex before, during, and after a simple voluntary action, that neuron
firing started increasing up to one second before the actual movement. This premovement brain
activity is called the readiness potential (RP). Libet et al. (1983) wanted to determine when,
prior to the actual movement, experimental subjects became consciously aware of deciding to
perform a movement, a simple flick of the wrist, for example. In these experiments, the RP
began on average .550 seconds before activation of the muscles in the wrist. Participants were
asked to specify exactly when they became aware of the conscious desire to move their wrists by
watching a moving spot of light on a clock face. The participants were asked to report the
position of the spot when they first became aware of the urge to act, which allowed Libet et al.,
to determine the timing of the perception of the urge. On average, the participants reported that
the urge to act occurred about .200 seconds before the muscle activation and about .350 seconds
after the onset of the RP. The fact that measurement of part of the subconscious preparation to
act, the RP, preceded the conscious decision to act by .350 seconds seems to rule out conscious
will as the initiator of the action.
Soon et al. (2008) have recently reexamined experimentally the nonconscious aspects of
decisions, in part because it has been argued that the time delay between the onset of the
readiness potential and the decision [in Libet et al.s experiments] is only a few hundred
milliseconds.[so] that potential inaccuracies in the behavioral measurement of the decision
time at such short delays could lead one to misjudge the relative timing of brain activity and
intention (Soon et al. 2008, p. 543). Using a more complex decision-making task than Libet,
Soon et al. wanted to determine whether any leading brain activity indeed selectively predicted
the outcome of the subjects choice (Soon et al. 2008, p. 543). Leading brain activity that
significantly preceded the subjects conscious awareness of making the choice, and that could
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allow the investigators to predict that choice, would provide substantial evidence of the
preconscious nature of choice. In fact, Soon et al. found cortical brain activity that predicted the
subjects decision up to 10 seconds before the decision reached conscious awareness, which
added considerable support to Libets finding that decisions are made before we become
conscious of having made those decisions.
The preconscious nature of decisions implies that at any given time our behavior is the
predetermined product of our preferences, our institutions and culture, and of course, our income
and the prices we face, as well as other components of the standard microeconomic constraint set
along with information that is a product of our current perceptions. In the context of Equation 3,
stable institutions enhance the creation of background knowledge, O(2B), further, stable
institutions in the form of secure property rights and contracts increase the instrumental
relationship between a current sacrifice ~h and a future gain 2B. Institutions facilitate the
creation of mechanisms that increase the odds of enhanced well-being in the future, O(2B|~h),
as well as mechanisms that link future gains 2B, to current sacrifices ~h. By so doing they
increase the likelihood ratio,L(~h| 2B), and simultaneously they make possible the very
cognitive capacity that enables us to enhance our future prospects by making current sacrifices.
7 Hyperbolic discounting, metacognition and metapreferences
A major finding in the experimental literature on both animals and humans is the use of
hyperbolic rather than exponential discounting in intertemporal choice.15 Hyperbolic
discounting results in intertemporal preference reversals, so that an individual who is offered a
choice today of receiving $50 in 100 days or $60 in 101 days will take the $60, but when the
options are moved forward, so that he can either have the $50 immediately or $60 tomorrow, he
15 See Ainslie (1985), Kim et al. (2008) and Laibson (1997), for an extensive discussion.
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chooses the $50. Because our neural mechanisms of choice evolved in a highly uncertain
environment with minimal to no secure property rights, the assertion here is that hyperbolic
discounting is the default mode for nonhuman animals and humans in novel, institution-free and
property right free environments. In humans, symbolic language, institutions, and personal and
social rule-following facilitate the inhibition of the tendency to take the immediately available
good that is a product of our evolutionary history. The evolution of big brains and symbolic
language supports the ability to inhibit the picking of the immediately available option and
makes long term planning possible; however, this ability also creates opportunities for lapses of
self-control. As we will see, metacognition, i.e., cognition about cognition, is necessary for the
conscious awareness of lapses of self-control, and without that awareness we would not perceive
self-control as a problem, or be aware of the issue.
Self-control problems exist when a choice is between a currently available good (say,
watching TV) and an option (doing homework) that requires bearing a current cost associated
with a deferred benefit, when we choose between two goods, where the preferred one has a
deferred cost and the other possibly a deferred benefit (e.g., chips vs. broccoli) or when we chose
between consuming and abstaining (having an additional drink at a party vs. not having one).
The default mode biases choice in favor of the currently available option because it is perceived
to be the less risky option. The circuits that facilitate the assignment of emotional values used in
action simulation also motivate the decision maker to act. Self-control is necessary to inhibit this
motive force. Self-control is a problem because motivation is strongest when a prepotent (i.e.,
highly valued) option is present in the decision-making environment: watching TV, the chips,
another beer. To resist these prepotent options, the force of motivation must be inhibited; self-
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control, then, requires the use of inhibition, and those who have problems with self-control have
problems inhibiting prepotent responses.
Some have argued that self-control and related problems occur because individuals have
meta-preferences (preferences over preferences): at one level they prefer chips, but they have
higher-level preferences for preferences that do not include chips. (see, George 1998 and Jeffrey
1974) Others have suggested a multiple selves view of self-control. (see, Posner 1995, and
various articles in Elster 1986). Before the party, one self favors a strict limit of two beers
during the evening, but at the party, having already consumed the two beers, the current self feels
certain of the desirability of having just one more.
16
In fact, the concept of self-control as well as
specific problems of self-control are a product of the human capacity for metacognition,
cognition about cognition, and cognition about past actions. We understand that we have a
problem with self-control at the metacognitive level. If we cant think at this level we dont
perceive that we have a self-control problem. Consider H, who is incapable of metacognition. If
H sees a cat across the street, seemingly without thought he runs after it. If H were capable of
metacognition he might perceive that his behavior is irrationalthere is no conceivable reason
for a well-fed agent such as himself to want to catch a cat, and by dashing across the street he is
likely to get hit by a car. However, H has no reflective moments in which he can consider the
risks associated with his behavior, so, as far as he is concerned, he has no self-control problem.
Humans can reflect on their thoughts and behavior, and thus consider the future consequences of
clogging their arteries with fat and failing to do their homework, a process that enhances self-
control.
16 These approaches tend to encourage the notion that one of the selves or preference sets is imposing an externalityon another, possibly leading to the conclusion that government regulation is necessary to correct the externalityproblem.
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Cultural evolution and the growth of knowledge contribute to problems of self-control.
In our EEA, things that are much more abundant and accessible with more certainty today, for
example, fat and salt, were very much more scarce in the past, and their consumption was likely
to always be fitness enhancing. Consequently, our motivational systems have evolved to
strongly kick in when goods with these attributes are available. Furthermore, addictive
substances activate the motivational system in a way that generates reward values that can
exceed the evolutionary fitness-enhancing value of these goods.
Increased knowledge allows us to become aware of new potential self-control problems
that we previously did not consider as problems. In the past, being overweight, eating high fat
meat, and smoking were not considered problems involving self-control because people were
unaware of the long-term consequences of their behavior. Thus, cultural evolution increases the
number and nature of problems of self-control in two ways: through economic growth that
significantly lowers the cost of a number of goods whose consumption can result in long-term
harmful consequences and increases their availability, and through new knowledge by which we
become aware of the harmful consequences of the consumption of these goods. This last shows
that self-control problems, in part, reflect how we perceive reality, not reality itself. In the
eighteenth century, being fat was a sign of wealth, and those who were fat did not consider that
they had a self-control problem because they were unaware of the adverse health consequences
of being overweight.17
Problems of self-control require three characteristics to exist: choices that
involve trade-offs between current and future options, the ability to reflect on the long-term
consequences of the consumption of these alternatives (i.e., to engage in metacognition) so that
that reflection can potentially influence that consumption, and finally, an awareness of the long-
term consequences of the consumption.
17 Of course, they didnt perceive that they were over weight in the first place.
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As a result of new information we become aware of the long term consequences of the
consumption of certain goods, and that awareness creates potential self-control conflicts. One of
the major difficulties in these situations is the lack of rapid direct personal experiential feedback
when either the costs or benefits of the consumption accrue in the distant future. However, as
more external data about these future costs and benefits they can over time be internalized as part
ofO(H) facilitating self-control.
8 How big of a problem is self-control?
8.1 Social facts and symbolic culture facilitate self-control18
An important factor that facilitates self-control is the cultural evolution of decision-making
environments, where the options at the time of choice are represented by symbols for the options
rather than the actual goods themselves. The evolution of human symbolic language ability and
metacognition allows us to increasingly avoid making decisions where the options are actually
present, reducing the problems associated with prepotent responses. The evolution of advanced
market economies has been accompanied by the evolution of a much more abstract, symbolic
decision-making environment, so that by making choices between, for example, saving and
consumption while not confronting actual consumption goods, we enhance our self-control. The
simple feature of making choices between symbols of goods rather then the goods themselves
enhances self-control.
Higher human cognitive ability rests on our ability to think symbolicallyprincipally,
that is, to think using languageand our stream of conscious thought is usually in the form of
18 Parts of this section are based on Gifford (2002a).
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language.19 The use of symbolic language allows us an additional degreeof separation, when
thinking, from the salience of goods in the current environment, over and above the inhibition
process mention above. It is this symbolic separation, coupled with lower-level inhibition, that
gives the feeling of separation between emotion and reason and that allows us to calculate the
implications of living in a complex social environment. The argument is that, along with the
inhibitory mechanisms that humans share with other animals, language and symbolic thought
provide humans with an additional degree of inhibition not usually available to other animals.
Several lines of evidence support this claim. First, as already noted, in experimental settings
animals display very high discount rates compared to humans.
Experiments by Boysen and Bernston (1995) and Boysen et al (1996), which were used
by Deacon to illustrate the importance of symbolic capacity for self-control. In the experiments,
chimpanzees that were [g]iven a choice between two different-sized piles of a desirable food
(like candy),consistently choose the larger pile, just as human children do. [The experiment
is then complicated] by giving the larger pile not to the chimp who chose it but to a second
chimp [the first chimp would only receive the larger pile when he chose the smaller one]. In
effect, one chimp was asked to choose the pile another would get, and by default, which would
be left for himself ([material in the brackets added for clarification] Deacon 1997, p. 413-414).
Human children have no trouble with this problem, [c]himps, however have extraordinary
difficulty discovering the winning strategy (Deacon 1997, p. 414). The chimps have great
difficulty with this task because the presence of such a salient reward undermines their ability
to use the stimulus information against itself. Being completely focused on what they want, they
seem unable to stand back from the situation, so to speak, and subjugate their desire to the
19 This isnt to suggest that all thinking, cognition, or choice relies on inner speech. Most cognitive activity is not inthe form of language and not conscious. Even at the highest levels our thoughts are not in the form of language, butrather language helps us keep track of those thoughts.
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pragmatic context, which requires them to do the opposite of what they would normally do to
achieve the same end. This is a very counterintuitive association for chimps to learn, because the
indirect solution is overshadowed by the very powerful influence of its mutually exclusive and
otherwise obvious alternative. The highlyrewarding nature of the stimulus also reinforces the
power of the competing association ([emphasis added] Deacon 1997, p. 414).
The chimps were then trained to associate different quantities of the desirable food with the
corresponding Arabic numeral. At the end of [the] training period, [the chimps] could fluently
move back and forth between a digit and the corresponding quantity. This can be considered as
the essence of symbolic knowledge
(Dehaene 1997, p. 37).
20
When making choices between
the symbolic representations (the numerals) of the larger and smaller piles the chimps were able
to choose the symbol representing the smaller pile (i.e., the smaller numeral) and thus receive the
larger reward (Deacon 1997, p. 414). By separating the saliency of the reward from the choice
process itself, by making choices between symbols of the rewards and not the rewards
themselves, the animals were able to discover the winning strategy. With their newly acquired
symbolic ability the chimps were able to make an optimal choice, one they were unable to make
without the choice involving the symbolic representation of the available options.
The third line of evidence supporting the claim that language increases inhibition comes
from cases of debilitating epilepsy in which patients are able to function after the severing of
their corpus collosumthe major communication pathway between the two halves of the
cerebral cortex. This severing prevents seizures from spreading from one hemisphere to the
other. In carefully designed experiments, differences in the behavior generated by the now
separated halves of the brain have been revealed. In comparing the performance of the language-
9 After years of training, when shown an Arabic numeral between 0 and 9 the chimps were able to choose, fromamong different sets of objects, the set that contained the number of objects that matched the numeral (Dehaene1997).
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dominant left hemisphere with that of the right hemisphere, Gazzaniga finds that the nonlingual
right hemisphere lives only in the thin moment of the present (Gazzaniga 1998, p. 54).
Finally, evidence comes from the behavior of profoundly deaf individuals who, for
various reasons, are not exposed to sign language or any other language instruction as children
and thus reach adulthood without any language at all. In a remarkable account of one such
individual, Susan Schaller (1991) describes attempts to teach language under these
circumstances: The most difficult taskwas schedules and time. The students only time was
the present (Schaller 1991, p. 197).
Language facilitates complex reasoning and contributes to the inhibition that allows that
reasoning to take place. It is this symbolic language facility that allowed humans to develop
complex social reality that itself is deeply symbolic in nature. However, evolution cannot
discard existing designs and start over from nothing, it can only build the new on top of the
oldthe old higher, biology-based time preference mechanisms are still built into the human
brain. These mechanisms must be overridden in decision making by the inhibition process,
which is significantly enhanced in humans by language. It is this divergence between the
cultural and biological rates of time preference that creates a potential internal nature vs. nurture
conflict leading to self-control problems. The problem of self-control is a product of the fact that
our lower-level time preference mechanisms evolved at a time when access to resources was
accompanied with a severe tragedy of the commons problem. Immediate acquisition of a
resource was always preferred because of the very high probability that it would be acquired by
some other agent if left in place. In the context of Equation 3, language puts the bird in the hand
and the two in the bush on more equal motivational footing.
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The symbolic nature of money also facilitates self-control. Estle et al. (2007) in an
experiment found that subjects discounted directly consumable rewards more steeply than
money, a result consistent with the findings of others (see, Odum and Rainaund 2003).
8.2 Personal commitments and rule following
Self-regulation refers to the process by which people initiate, adjust, or terminate actions
to promote attainment of personal goals, plans or standards. The process has three important
components: (1) having clear standards of how things should be, (2) comparing ones actual state
to a desired state (as defined by the standards), and (3) overriding responses to bring about
change when the current state falls short of the desired state (Nenkov et al. 2008, p. 127).
Metacognition facilitates self-regulation because: it makes possible the process of the
formation of clear standards (to have only two beers at the party, for example), it also makes it
possible to recognize whether or not those standards are being met, and finally, it facilitates the
adjustment of behavior accordingly. Metacognition and self-regulation facilitate what
psychologists call rule-following, and, though it does not eliminate all problems of self-control,
commitment and rule-following make self-control easier. Barkley discusses the role of rule-
following and inner speech in this capacity:Rules are strategies that, once formulated, can be used to guide behavior more efficiently
and effectively.Internalized speech, along with the sense of past and future afforded by working memory,
combine to give the individual the capacity to understand and comply with commands orrules that have prolonged references. That is, such instructions or rules make reference to abehavioral performance at a place and time temporally distant from the temporal now inwhich the command or direction is given (Barkely 1997, p. 246-247).
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Personal rule-following is a form of self-commitment that facilitates the inhibition of prepotent
responses and allows us to reflect on the implications of violating those commitments.
Commitment and rule-following in a particular situation can eventually take the form of habits
that further reduce the motive force of prepotent responses.
Rule following, where rules overtime become internalized as background knowledge
part ofO(2B)and the abstract nature of our social realty facilitates self-control. In self-control
situations the structure of the external environmentthe good that elicits a strong prepotent
response, but which are also associated with long-term costscan automatically trigger rule
following. This allows us a degreeof separation from the salience of goods in the current
environment that enhances self-control.
Advanced market systems facilitate self-control because they are complex symbolic
social constructs where many of our options, at the time of choice, are represented by symbols
rather than the actual goods. By securing a system of property rights, facilitating long-term
contracts and by enhancing inhibition, language, culture and other institutions increase the odds
that ~h will lead to 2B, and they also lead to the creation of institutional mechanisms that, along
with habits and rule-following, enhance self-control. Savings accounts and automated
deductions are institutional mechanisms that enhance self-control. Rabinovich and Webley
(2006) find that in Belarus, because banks are often not readily available, individuals transfer
part of their income into a foreign currency for self-control purposes. Finally, Oaten and Cheng
(2007) show that practice increases self control. This result is consistent with the proposition
that rationality is a product of ongoing embodied and embedded cognition, but not with a top-
down central planner view of rationality.
8.3 Regret, ex ante and ex post
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Time is scarce: we all have more to do than time to do it in. There is a tendency to have queues
of various types of projects, research projects, books to read, projects to do around the house,
various alternative things to do when going out for the evening. In fact, there is probably an
evolutionary advantage to scheduling more to do in a given period of time than you are likely to
accomplish. Since allocating ex ante exactly the amount of time necessary to complete the
various projects is difficult, the options are to plan to do too much in a given period of time or
too little. We are likely to accomplish more if we plan to do too much, rather than too little.
This implies that even individuals who are optimal decision-makers may feel that they did not
accomplish all that they planned to do and, as a result, they will feel regret. Furthermore, since
our ex ante plans do not always work out as we expect, we may feel regret that we did not select
another option. Ex ante we maximize expected utility, ex post we are from time to time
disappointed with the outcome and wish we had selected another option. This is an example of
how regret operates as a learning signal, facilitating, if necessary, a readjustment of
expectations, but it does not necessarily signal suboptimal decision making. We feel ex post
regret even if ex ante decision-making is fully rational.
8.4 Intertemproal regret reversals
Kivetz and Keinan provide evidence that [w]hile yielding to temptation can certainly be
harmful, [it is also true] that overcontrol and excessive farsightedness (hyperopia) can also
have negative long-term consequences (Kivetz and Keinan 2006, p. 273). Kivetz and Keinan
find that over time individuals regret missing out, by not taking advantage of indulging in
actions that bring more immediate pleasure. In thinking about choices made in the distant past,
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specifically, studying rather then traveling during winter break, university alumni at a reunion
expressed significantly more regret over not indulging than current students thinking about the
same alternatives confronted last week (Kivetz and Keinan 2006, p. 278). Kivetz and Keinans
sample consisted of 31 travelers waiting for domestic flights at a major airport and 32 park
visitors (surveyed about actual past trade-offs between work and pleasure), as well as 69 current
students and 24 alumni (surveyed about actual trade-offs between travel vs. studying during
winter break). All exhibited regret reversals with the passage of time, feeling self-control
regrets for recent indulgences and missing-out regrets over more distant sacrifices of enjoyment.
It seems that, with time, the regret over self-indulgence for choosing immediate pleasure vs.
long-term interest is replaced by regret about the things we missed when we sacrificed
immediate pleasure for long-term gains. Indeed, in the long run, people of every age and in
every walk seem to regret nothaving done things much more than the regret things they did
(Gilbert 2005, p. 197).
8.5 Saving and rationality
Perhaps the strongest claim of irrational impulsivity is the assertion that Americans are
under saving. From the 1st quarter of 2005 through the first quarter of 2008 the U. S. personal
savings rate averaged will below 1%.21 However, this data does not include asset appreciation,
and increases in human capital,22
in fact, using 1996 data from the Health and Retirement Study
(HRS) of household nearing retirement Scholz et al. (find strikingly little evidence that HRS
households have undersaved (Scholz et al. 2006, p. 637). They find that 84.4 percent of
households meet or exceed their [optimal] wealth targets (and most of those who are below miss
21 U.S. Department of Commerce data.22 Just looking at Bachelors degrees and higher, ignoring post-secondary trade schools, and other post-secondaryeducation, the percentage of the population 25 years and older with college degrees has increased from 4.6 in 1940to 24.4 in 2000 according to Census data.
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by a relatively small amount) (Scholz et al. 2006, p. 637). There obviously is variation in
meeting retirement wealth targets, with those who miss their targets tending to fall in the lower
income categories, but these results suggest that on average that near retirement household
wealth is close to optimal.
8.6 Natural selection and near optimal decision making
Natural selection does not necessarily always lead to optimal solutions to the problems that
confront species, but the optimizing perspective is useful because the existence of multiple
causal means to a given end is the norm in selection-driven processes (Ross 2005, p. 361).
Gintis argues that [g]eneral evolutionary principles suggest that individual decision making can
be modeled as optimizing a preference function subject to informational and material constraints.
Natural selection leads the content of preferences to reflect biological fitness (Gintis 2006, p. 4).
If we take what Dennett (1987, 1995) calls the intentional stance, whereby we treat the
mindless evolutionary processes of random variation, inheritance and selection as an intentional
goal-seeking system, we can consider that brains, organs, body structure and behavior constitute
solutions to problems. Natural selection is massively parallel, with virtually no time constraint,
creating the ability for a very large number of trial and error attempts at solutions to those
problems. And considering that random perturbations to the process can likely bump it off of a
local optima, allowing the continued search for a global one, suggests that brains are designed
by natural selection to solve specific problems confronted by our ancestors and that design is
likely to be near optimal solutions to those problems.
8.7 Individual variation in self-control
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The discussion above suggests that, on average, we may not be as poor at self-control as it is
commonly assumed; however, like most human traits, there is variation across individuals in
their level of self-control. Specifically: though most individuals are likely to be clustered around
the mean and may have close to optimal self-control, for those on both the tails there will be less
than optimal performance because too much self-control is as suboptimal as too little.
9 Conclusion
If we can assume that natural selection has given us at least a close to optimal decision-making
apparatus, then why does it fail so dramatically in the laboratory? In part, it fails because our
cognition is embodied and necessarily imbedded in our cognitive environment. The human
environment is to a large extent social, and it is a product of our ability to create social facts that
facilitate the social construction of institutions and culture that are products of our symbolic
language capacity. Nonconscious access to these components of our cognitive process are often
primed by the decision-making environment. This background knowledge, O(2B), in the context
of Equation 3, may not be not be primed in the more sterile lab environment, resulting in
suboptimal decision making in that setting.
Furthermore, the evolution of our large brains and symbolic language capacity has
directly facilitated increases in self-control, since these features allow choices to be represented
symbolically. By making possible the creation of social facts, they allow for the construction of
our symbolic culture and institutions that made those plans possible cognitively and also make
them viable by making it possible for current sacrifices to consistently result in long term net
gains.
A neural model supported by a significant amount of neuroscience evidence supports the
view that the brain employs Bayesian strategies at close to optimum levels in decision making.
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Embedded in our symbolic culture and institutions, these decision-making processes use
simulations of possible futures to estimate the discounted expected benefits and costs of the
various alternatives. These net values reflect the outcomes of past options chosen and the missed
opportunities from options not chosen; the result of options chosen is the generation of a
prediction-error learning signal, and the result of options not chosen is generation of a fictive
learning signal. The fictive signal can generate regret when the outcome of options not chosen
seems to be better than the outcome of the option actually chosen. The evidence suggests that,
together, the prediction error and fictive learning signals do a good job of guiding behavior
towards near optimum outcomes. Both first-hand knowledge and second-hand knowledge are
used in decision simulations; however the first, provided by direct experience, is often more
reliable.
Feelings of regret are often used as evidence for a general lack of self-control, but, in
fact, there are several reasons why regret may not provide that evidence. First, since we often
plan to do more than is possible in the time available, we may feel regret over things not
completed even when our decision making is optimal. Second, because we always make
decisions with less than perfect information, ex ante, optimal decisions can lead to less than
satisfactory outcomes ex post, resulting in regret. Finally, over time, regret about options chosen
changes to regret about options not chosen. These regret reversals suggest that, over time,
individuals often wish they had indulged in short-run pleasures more rather than less often,
weakening support for the position that short-run feelings of regret provide evidence of
suboptimal self-control.
Finally, metacognition made possible by our large brains and symbolic capacity
facilitates personal and social habits and private and social rule-following that enhance self-
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regulation. That rule-following and commitment are forms of self-regulation, which are
enhanced by practice, is supported by the evidence that, when the value of all assets are
considered, individuals do not seem to be consistently under-saving for retirement.
To sum up, after examining the neural mechanisms of embedded and embodied cognition
and the supporting evidence it does not seem that the case in favor of widespread and consistent
irrationality in temporal decision making is strong.
Acknowledgements I thank the editor, Janet Landa, for her many helpful comments and twoanonymous referees for their useful comments and suggestions.
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