Uncertainty and denial: a resource-rational model of the value of
information
Emma Pierson and Noah Goodman
Department of Psychology, Stanford University, California
September 16, 2014

Abstract
Classical decision theory predicts that people should be indifferent to information that is not usefulfor making decisions, but this model often fails to describe human behavior. Here we investigate one
such scenario, where people desire information about whether an event (the gain/loss of money) will
occur even though there is no obvious decision to be made on the basis of this information. We finda curious dual trend: if information is costless, as the probability of the event increases people want
the information more; if information is not costless, people’s desire for the information peaks at anintermediate probability. People also want information more as the importance of the event increases,
and less as the cost of the information increases. We propose a model that explains these results, based on
the assumption that people have limited cognitive resources and obtain information about which events
will occur so they can determine whether to expend effort planning for them.

When do we want to know? The question rises ubiquitous in an uncertain world, in manifestations
both trivial–should you check the calorie count on that decadent croissant?–and momentous–should you
learn whether you carry the Huntington’s gene? [1-3] Computing the value of information, V OI, is thus a
cognitive task of great importance. Classical decision theory offers one definition of V OI: take the difference
in expected utility of the optimal decision made with the information (EUinf o ) and the optimal decision
made without the information (EUnoinf o ) [4]. More formally, if the information is the value of a random1variable X that takes values xi with probability p(xi ), and our decision is which action a to take to maximize
a utility U that is a function of X and a, then V OI is the difference between the expected utilities with andwithout information:

V OI = EUinf o − EUnoinf o =Xip(xi ) max U (xi , ai ) − max
a

ai

X

p(xi )U (xi , a)i

V OI is non-negative, which reflects the intuition that more information never makes your decision worse.
Learning the croissant’s calorie count might guide you to a salad instead [5]; learning you will not get
Huntington’s might make you delay a hasty marriage. The non-negativity of V OI implies an important
corollary: we should never not want information, and should be indifferent to it if and only if it cannot
affect our decisions. As a description of human behavior, however, this proves false in two ways. On the one
hand, people often desire information even when it will not affect their decisions: for example, women at
risk for a mutation that predisposed them to breast cancer remained equally likely to get tested even when
it would not affect their medical decisions [6]. People will delay a decision until they obtain informationeven when they would make the same decision regardless of the information [7]. Even animals sometimes
display a preference for information even when it will not affect their decisions, possibly because of the same
biological circuitry that motivates reward-seeking [8]. On the other hand, people are often strongly averse
to information: they often do not want to learn that someone else got a better deal on an recent expensive
purchase [9]; or the gender of their child before the birth [10]. While these deviations from classical decision
theory have been dismissed as “irrational”, they indicate the model’s inadequacy as a descriptor of humanbehavior.
Here, we explore one region that the classical model does not explain: we investigate a situation where
information has no obvious effect upon decisions but people nonetheless care deeply about it. We ask people
to imagine the following scenario: They are locked in a room for an hour. At the end of the hour, there
is some chance they will gain or lose a sum of money. We then ask them if they would prefer to be told
at the beginning of the hour whether they will gain or lose the money. There is no obvious decision to bemade based upon this information: either way, people are locked in the room for an hour. Even if there are2

decisions to be made based on the loss or gain of money, they cannot be made until the person leaves theroom, when they will have the information anyway. And yet people are not indifferent to the information.
We present a series of mathematical models that explain this lack of indifference. We begin with theclassical model of a perfect Bayesian decision theorist. We then successively refine the perfectly rationalmodel by adding a series of “empirically validated irrationalities” to our Bayesian agent. The first and mostimportant refinement flows from our central thesis that because people have limited cognitive resources, theyobtain information about whether events will occur so they can determine how to allocate cognitive resources
to planning for them. The “decision” to be made here, which gives information its value, is a meta-cognitive
one: how much effort to devote to planning for an event. Importantly, we believe that this goal of devoting
the optimal effort to planning is a computational goal that guided the evolution of our cognitive processes, notnecessarily a motive that people are consciously considering when they decide whether to obtain information.
People may say, for example, that they wish to obtain the information because they feel nervous, but thisnervousness is merely the brain’s way of motivating them to pursue information in situations where it tends
to be valuable for planning. (The fact that the same circuits are involved in both information-seeking andreward-seeking [8] also indicates the deep level on which information seeking is rewarded.) Put anotherway, our explanation operates on Marr’s computational level [11], describing a goal of cognition; it does notnecessarily operate on his algorithmic level, explaining the proximate means by which this goal is carriedout.
We further refine our model using results from prospect theory [12]: first, people are risk averse, withconcave monetary utility functions; second, people are loss averse, finding losses of money more psychologically important than gains; third, people perceive probability non-linearly, overestimating low probabilities
and underestimating high ones. We then conduct two experiments testing the empirical predictions of thesemodels.

1ModelsWe present a series of models for V OI in the scenario described above: a person is locked in a room for anhour and is told there is a probability p that they will lose or receive an amount of money D when they leave3the room at the end of the hour (and a probability 1 − p that nothing will happen). They can pay a cost C
to learn at the beginning of the hour whether they will lose/receive the money. They indicate their desire for
information on a 5-item Likert scale from “Strongly prefer not to know” to “Strongly prefer to know”. For
each model, we link V OI to the fraction of participants responding in each Likert category using an ordinal
probit model [13]: the fraction of participants responding in Likert category i is Φ(ci −V OI)−Φ(ci−1 −V OI),
where Φ is the normal cumulative distribution function, c1 , ..., c4 are parameters of the probit model, andc0 = −∞, c5 = ∞ for convenience. We begin with the perfect Bayesian decision theorist model of V OI andsuccessively extend it to produce four more realistic agents. After presenting the models, we explore how
they fit the results of two experiments.

1.1

Model 0: Perfect Bayesian agent

Classical decision theory predicts thatV OI =X

p(xi )maxai U (xi , ai ) − maxaiXp(xi )U (xi , a)iwhere xi is an indicator variable that is one if the person will lose/receive the money and zero otherwise.
Since there is no action that can be taken while in the room based upon this information that cannot betaken upon leaving it, the maximization over ai is trivial–U (x, a) = U (x)–and V OI is zero.

1.2

Model 1: Resource-rationality

Our most important refinement to the perfect Bayesian model flows from the basic observation that because
people have limited cognitive resources, they obtain information about whether events will occur so they candetermine whether to plan for them. If an event is not going to occur, there is no need to plan for it; if an
event is going to occur, receiving this information at the beginning of the hour is advantageous because it
gives you more time to plan.
We formalize this as follows. We assume that people can choose to devote computational resources
E ∈ [0, 1] to planning for an event; if the event occurs, this planning pays off with utility gain G(E),where G(E) is monotonic increasing in E and G(0) = 0. If the event has a probability p of occurring,4

the expected utility of planning for it is pG(E) − E: the possible gain of planning for it minus the certain
cost of expending computational resources. Thus, a resource-rational agent who believes an event has aprobability p of occurring will devote E ∗ = arg maxE (pG(E) − E) to planning for the event, with expectedutility U ∗ = maxE (pG(E) − E).
We assume the agent has a choice between three options:
1. Obtain information: If the agent obtains the information, with probability p they find out the event
will occur, plan for it, and gain utility maxE (G(E) − E); with probability (1 − p) they find out theevent will not occur, devote no effort to planning for it, and gain utility 0. Thus, the expected utilityof information is p maxE (G(E) − E) − U (C), where U (C) is the utility lost by paying C to receive theinformation and U (0) = 0.
2. Live in uncertainty: If the agent does not obtain the information and plans based on their belief thatthe event has probability p of occurring, they devote effort arg maxE (pG(E) − E) and gain utility
maxE (pG(E) − E). Note that if C = 0, this option will be inferior to option 1 for p ∈ (0, 1).
3. Live in denial : If the agent does not obtain the information and does not plan, they do not have todevote resources to computing the maximization or to keeping the event in the back of their mind;
because this devotion of resources has an opportunity cost, we assume the freeing up of computational
resources has a utility M .
The value of information is the difference between the utility of (1) and the max of the utilities of (2)and (3):

V OI = p max(G(E) − E) − U (C) − max(max(pG(E) − E), M )E

E

The intuition behind this model is simple. If information is free (C = 0), the only reason not to get it isbecause one does not want to plan for the event at all (“live in denial”). Since it makes the most sense toplan for events that are most probable, information grows more desirable, and denial less attractive, as theevent grows more probable. On the other hand, if information is not free, there is another potential reason
for not getting it: one still intends to plan, but the confirmation is too expensive (“live in uncertainty”).
5Thus, our model predicts that desire for information should increase essentially monotonically in probability
if information is free but should exhibit a more pronounced dropoff if information is not free. This is in fact
what we observe (Fig. 1).

Mean Desire to Know

0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0

−0.3

Mean Desire to Know

Model
Experiment

−0.4

.01

.1

.5

.9

.99

.1

.5

.9

.99

Probability of Event

Model
Experiment

−0.5
−0.6
−0.7
−0.8
−0.9
−1

.01

Probability of Event

Fig. 1: In both model and experiment, desire for information increases monotonically in probability if
information is costless (top graph), but exhibits a dropoff if information is not costless (bottom graph). The
difference between the two trends is significant (p = .002, ANOVA). Top: Results of Experiment 1, costless
information. Bottom: Results of Experiment 2, non-costless information. Vertical axis is mean response on a
Likert scale that ranges from 2 (“Strongly prefer to know”) to -2 (“Strongly prefer not to know”).Model error
bars are produced by bootstrapping samples and refitting each model; experiment error bars are standard
error of the mean.
We explore two methods of modeling the effect of varying the amount of money at stake, D. First, we
assume that the utility of planning for an event scales with the importance of the event, since more important
events are more important to plan for; thus, the utility of option (1) becomes p maxE (f (D)G(E)−E)−U (C),where f increases in the amount of money to be gained or lost, and the utility of option (2) becomes
maxE (pf (D)G(E) − E). Second, we assume that the effort of planning for the event also increases with theimportance of the event being planned for: it not only pays off more, but also requires more effort, to planfor one’s approaching wedding than to plan for an approaching bus. Thus, the utility of option (1) becomes6

p maxE (f (D)G(E) − f (D)E) − U (C), and the utility of option (2) becomes maxE (pf (D)G(E) − f (D)E).
We refer to the former choice as “unscaled efforts” and the latter choice as “scaled efforts”; the latter fit ourexperimental data slightly better.

1.3

Model 2: Risk aversion

While a risk-neutral agent would have a linear cost function [14], U (C) = U0 C, numerous authors have foundthat people are risk averse [15], so we refine our model by making U (C) concave. Similarly, while a non-risk
adverse agent would set f (D) = f0 D, we use a concave f . To restrict the space of models and prevent
overfitting, we assume both f and U are logarithmic, a standard choice for monetary utility functions [16],setting f (D) = f0 log(1 + |D|) and U (C) = U0 log(1 + C).

1.4

Model 3: Loss aversion

People are are loss-averse, finding losses of money more psychologically important than gains [17-19]. Therefore, we use f (D) = f0 log(1 + |D|)(1 + δ{D < 0}L) where δ{D < 0} indicates whether the agent is losing or
gaining money and L is a loss aversion parameter. (Note this adds an additional parameter to the model.)1.5

Model 4: Non-linear probability weighting

People’s perceived probability of an event, w, is non-linear in the actual probability of the event, p: specifically, people sometimes overestimate the probability of unlikely events and underestimate the probability ofvery likely events. [20-22] use parameterizations of the form w(p) = pβ /(pβ + (1 − p)β )1/β , estimating similarvalues of β from experiments; we use the estimate in [17], β = .56.

2

Experiment 1: Costless Information

For our first experiment, we set the information cost C at zero (information was free) and varied the monetaryamount to be gained or lost, D, and the probability that the event would occur, p.

7

2.1

Methods

Three hundred survey participants were recruited via Mechanical Turk and given the following prompt:“Imagine you will be locked in a room for an hour. At the end of the hour, you will be allowed to leave
the room. There is a chance that something will happen to you when you leave. In each of the following
situations, you will have to decide whether you want to be told at the beginning of the hour what will happen
to you when you leave the room.” Participants were then given 30 situations of the form, “There is a [p]chance that [X]. Here is your choice: do you want to be told at the beginning of the hour whether [X], or
do you want to wait until the end of the hour to find out?” The fourteen events X were “you will get D”
or “you will get D taken away” where D was $10, $50, $100, $500, $1,000, $5,000, and $10,000. The fiveprobabilities p were 1%, 10%, 50%, 90%, and 99%. Participants answered using a 5-item Likert scale ranging
from “I would strongly prefer not to be told” (scored as -2) to “I would strongly prefer to be told” (scoredas 2). Each participant was given 6 randomly chosen X and for each X answered all five probabilities p in
random order. (Because subjects had no contact with experimenters, it is unlikely that they chose theiranswers merely to “appear rational” to experimenters.)
This research was reviewed for ethical treatment of human subjects by the Stanford University Administrative Panel on Human Subjects in Medical Research (IRB Number 349, Protocol ID 19974). It wasdetermined to be in an exempt category, allowing the requirement for documented consent to be waived.
Instead, participants provided consent by clicking “Start” after reading the following information: “By answering the following questions, you are participating in a study being performed by cognitive scientists in
the Stanford Department of Psychology. If you have questions about this research, please contact Emma
Pierson at emmap1@stanford.edu or Noah Goodman, at ngoodman@stanford.edu. You must be at least 18
years old to participate. Your participation in this research is voluntary. You may decline to answer any or
all of the following questions. You may decline further participation, at any time, without adverse consequences. Your anonymity is assured; the researchers who have requested your participation will not receive
any personal information about you.” All participants provided consent by clicking through this page.

8

2.2

Results and Discussion

The first result of Experiment 1 was that people preferred to receive information even though there was no
obvious decision to be made, a result not predicted by classical decision theory; the mean Likert scale response
across all probabilities and monetary amounts was .44, significantly different from 0 (p < .001, t-test). We
observed three other significant trends (p < .001, ordinal regression). People’s desire for information increased
1. As the probability of the event increased
2. As the amount of money they might lose or gain increased
3. For losses as opposed to gains of money
These trends are consistent with, and extend, previous results. [23] found that people’s desire to learn
the outcomes of lotteries early was greater for losses as opposed to gains of money, consistent with trend(3). [24] found that if people were threatened with a shock and given the choice of listening to a informative
tone that would warn them when the shock was about to occur or listening to music, they were more likely
to choose the informative tone as the magnitude of the shock increased, consistent with trend (2).
Trend (1) is predicted by Models 1-4 though not by Model 0. If information is free, the only reason not
to get it is to “live in denial”: if one intended to plan for the event, it would always make sense to find out
for sure whether or not it would occur in order to devote the optimal resources to planning. For C = 0
and p ∈ (0, 1), we have p maxE (G(E) − E) − U (C) > maxE (pG(E) − E), so “obtain information” is always
preferable to “live in uncertainty”. Thus, the fraction of the population obtaining information is essentiallythe fraction for which p maxE (G(E) − E) > M , which increases monotonically in p. (As p gets very close
to 0 or 1, this no longer holds true, since the utility difference between “obtaining information” and “live in
uncertainty” goes to zero, but this end behavior does not matter for most of the probability range, includingthat studied in our experiment.) Trend (2) is also predicted by Models 1-4 because more important events,
involving larger sums of money, are more important to plan for. Trend (3) is predicted by Models 3-4, which
incorporate loss aversion, making losses of money more important than gains.
We assessed the validity of our models by fitting them to our experimental results. Our parameters were
M , the utility of remaining in denial; f0 , how quickly the utility of planning increased with the amount9

at stake; and L, the loss aversion parameter, how much more important losses were than gains. (Notethat Model 0 had no parameters besides the regression parameters of the probit model, Models 1-2 had 2parameters, and Models 3-4 had 3). We chose a planning utility function G(E) = E(2 − E), where E ∈ [0, 1],
because it was concave (reflecting the diminishing marginal returns of additional planning); the results ofthe model were largely insensitive to choice of G.
The correlation ρ between fitted and modeled average responses on the Likert scale improved for eachmodel, rising to .73 for Model 4. The most dramatic improvement, unsurprisingly, occurred between Models
0 and 1, reflecting the importance of the basic resource-rationality insight; the next largest gain was between
Models 1 and 2, reflecting the importance of risk aversion.
To select the best model (Fig. 4), accounting for the fact that later models used more parameters, we
computed the likelihood of the data for each model and used the BIC score to penalize complexity: the score

Amount of Money at Stake

Fig. 2: Costless Information, Experiment 1. Top left: Desire for information as a function of probability,
averaged across all monetary amounts. Vertical axis is mean response on a Likert scale that ranges from 2(“Strongly prefer to know”) to -2 (“Strongly prefer not to know”). Top right: Desire for information as afunction of amount of money at stake, averaged across all probabilities. Bottom left: Desire for information
for all pairs of probability and monetary amount. Larger dots indicate a greater desire for information.
Bottom right: Modeled vs experimental values for all probabilities, monetary amounts, and Likert categories.

10

for each model was log L − d2 log N , where L was the likelihood under the probit model, N was the numberof datapoints and d was the number of model parameters (including probit parameters). Model 2 had thehighest BIC score (thus, all plots of model results in Figs. 1, 2, and 3 come from Model 2) substantiating
the observation, discussed above, that the most important improvements to the perfect Bayesian model wereresource-rationality and risk aversion, with loss aversion and non-linear probability weighting providing moremarginal gains. The additional parameter added by Models 3-4 is penalized by the BIC score, explaining
why these models were not favored; adding in the loss aversion parameter did improve the fit to the data,
however, so our results are consistent with prospect theory.

3

Experiment 2

For our second experiment, we varied the information cost C and the probability that the event would occur,p.

3.1

Methods

The same prompt as in Experiment 1 was used, except participants were asked whether they would want
the information if they had to pay $0, $1, $5, $10, $100, $1,000, or $2,000 at the beginning of the hour tolearn whether they would gain or lose $1,000 at the end of the hour.

3.2

Results and Discussion

We observed two trends (Fig. 3):

1. Desire for information decreased as the cost of information increased (p < .001, ordinal logistic regression).
2. Unlike in the first experiment, where desire for information increased monotonically in probability,
if information was non-costless desire for information peaked at an intermediate probability. The
discrepancy between this trend and the trend in Experiment 1 was significant (p = .002, ANOVA).
The decrease in desire to know between the 50% condition and the 90% and 99% conditions was also11

Fig. 3: Non-costless Information, Experiment 2. Top left: Desire for information as a function of probability,
averaged across all costs. Top right: Desire for information as a function of the cost of the information,
averaged across all probabilities. Bottom left: Desire for information for all pairs of probability and cost.
Bottom right: Modeled vs experimental values.
significant (p = .05, p = .005, t-test). (Although the statistical significance is clear, the effect size wassmall, which should be kept in mind in interpreting the results.)(1) is unsurprising; (2) is more interesting. Models 1-4 explain the discrepancy between probability
trends for costless and non-costless information as follows: if information is free, people’s only reason forrejecting it is if they choose to “live in denial”; if information is not free, people may prefer to “live in
uncertainty” for high probability events and to “live in denial” for low probability events. Thus, the desire
for information increases essentially monotonically for costless events (at least until p is very close to 1) but
not for non-costless events (Fig. 1).
We fit our model using an ordinal probit model as described in Experiment 1. The fit for Models 2-4 wasvery good (ρ = .96). As in Experiment 1, Model 2 had the highest BIC score (Fig. 4), so all plots shown
are for Model 2.

12

4

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BIC Score, Exp 1

Correlation, Exp 1

1

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Unscaled Efforts
Scaled Efforts
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Fig. 4: A comparison of all models by correlation with experimental values and BIC score. Model 2 has thehighest BIC score in both experiments, with the most significant gains in both correlation and BIC scoreaccruing between Models 0-2; the improvement is particularly dramatic in Experiment 2. Scaled efforts
slightly outperforms unscaled efforts. Error bars are produced by non-parametric bootstrap.

4

Conclusion

In this paper, we examine situations in which the classical decision theoretic model predicts that people
ought to be indifferent to information because it cannot affect their decisions, but in which people nonethelessexhibit striking preferences. If information about whether an event will occur is costless, people prefer toreceive it, with their desire increasing in the probability of the event and the amount of money at stake; if
information is non-costless, people’s desire for information peaks at an intermediate probability, showing aslight decrease thereafter, and decreases in the cost of the information.
We advance a series of models of how people value information that explain these trends as the classicaldecision theoretic model cannot. Like the classical model, we assume that people want to obtain information
to make optimal decisions, but we extend the model in two ways. First, we broaden the sense of “decision” toinclude the meta-cognitive decision of how much effort to put into planning for an event. Second, we assume
that people have limited cognitive resources, and that there is a cost to planning for or thinking about an
event. Our results also provide another example of the utility of prospect theory, particularly the fact thatpeople are risk-averse, with concave monetary utility functions; we also saw evidence of loss aversion andnon-linear probability weighting, although these provided much more marginal gains.
This model provides a framework for understanding when and why people engage in the importantcognitive task of obtaining information. It explains why they obtain information about whether an event13

will occur even when there is no obvious decision to be made–so they can determine how much effort toput into planning for the future in which the event happens–and also provides two reasons people have forrejecting information, which we refer to as the desires to “live in denial” or “live in uncertainty”. Living
in denial is attractive when the probability of an event is low, and the most efficient allocation of cognitive
resources is to not think about or plan for it at all; living in uncertainty is attractive when the probability ofthe event is near zero or one, and the residual uncertainty may more safely be ignored in determining how
much to plan for an event. Living in denial may be preferable regardless of whether information is costless;living in uncertainty is only preferable if information is non-costless. Intuitively, if you are told that you
have a 90% chance of having a deadly cancer, and there is a free test that will confirm whether you haveit, your motives for rejecting the test would be very different than your motives for rejecting a test thatcosts $100,000. If you reject the free test, you are probably planning to live out your days in denial on thebeach; if you reject the expensive test, you are probably planning to ignore the residual uncertainty and get
chemotherapy.
One potential complication in interpreting our experimental results is that what participants imagine
they will do may differ from what they will actually do. While it seems likely that the two are similar,
what people imagine they will do is in itself important to observe, since many situations require us toimagine whether we would want information: for example, if you are entering into a non-exclusive romantic
relationship, you may have to tell your partner ahead of time whether you would want to know if they are
pursuing other people. Further, if what people imagine they will do deviates systematically from what they
actually do, that in itself is a phenomenon worthy of more investigation.
In setting up our experiment, we chose a locked room because it provided a scenario in which the subject
was clearly unable to act on the information, and we chose receipt or loss of money rather than some otherevent because it allowed us to manipulate the effects of increasing the amount of money. Future work mightinvestigate related scenarios in which the reward or loss was altered or subjects were allowed to do somethingother than wait in a locked room. The latter question is particularly interesting because being locked in aroom with nothing to do for an hour is a potentially aversive scenario, and subjects’ reactions might change
if they were allowed to distract or otherwise engage themselves.

14

We note that while both our experimental observations and our model are novel, they do represent an
intriguing unification of previous work on why people choose to obtain information: the two motives ourmodel predicts for information avoidance are both similar to previously postulated motives. [25] proposesa model that predicts that the value of information about whether an event will occur will increase as theevent becomes more “salient”. All else being equal, higher probability events will be more salient (harder toignore)–for example, people threatened with an electric shock have higher heart rates as the probability ofthe shock increases [24]–so this model may be thought of as predicting the monotonic increase in V OI as afunction of probability characteristic of the “live in denial” motive for information avoidance. Meanwhile,[26-28] have proposed analyses that measure V OI in terms of information theoretic measures like probability
gain, KL distance, and Bayesian diagnosticity: these measures assign information the greatest value when
it most reduces uncertainty, yielding the non-monotonic trend characteristic of the “live in uncertainty”
motive. Both of these approaches are plausible, but neither can explain the whole of our results: our model
thus provides a unification of previous work.
An alternate explanation for our experimental results comes from psychological expected utility theory[29], proposed by Caplin and Leahy, which extends Kreps and Porteus’ dynamic choice theory [30]. Under
Caplin and Leahy’s model, people react to uncertainty with emotions like anticipation or anxiety and seek
to maximize the utilities associated with these emotions. For example, a sports fan who is risk adverse
might still place a bet on their favored team because doing so increases the pleasurable anxiety associatedwith watching the match. This model explains, for example, Lowenstein’s finding [31] that people will pay
more to delay (and thus anticipate) hypothetical kisses with movie stars, but do not want to delay events
like electric shocks, which are unpleasant to anticipate. One might similarly try to explain our experimental
results by speculating that people feel anxiety or anticipation associated with the potential gain or loss ofmoney. Why then do we need our planning-motivated model? First, in order for Caplin and Leahy’s model
to make predictions, one must derive a form for the utilities associated with anticipation or anxiety; ourmodel provides concrete predictions based on minimal assumptions. More importantly, saying that people
feel anxiety or anticipation begs the question of why they feel these emotions. Our model provides the
explanation: anxiety or anticipation are the proximate means by which our minds accomplish the ultimate

15

goal of making us pursue information that helps us optimize planning. Put another way, our model provides
an explanation on Marr’s computational level [11], complementing Caplin and Leahy’s explanation on thealgorithmic level. We think it is entirely possible that some of our experimental subjects were responding toanxiety or anticipation, not to a conscious desire to plan, but our model provides the ultimate explanation
for why they felt these emotions and also makes concrete behavioral predictions.
More broadly, our model is another manifestation of the hypothesis that people are resource-rational:
that is, they make decisions that are utility-maximizing given that cognitive resources are limited. This
perspective has frequently proven useful in the past [32]: we are not perfect utility-maximizers, but agentsstruggling with limited resources to make the best sense we can of a complex and uncertain world. We
constantly decide when to reduce that uncertainty by pursuing information, and those decisions will only
become more important as information becomes more available. From our increasingly sophisticated ability
to divine our pasts and futures from our genomes (do you want to know whether your father is really yourfather, or what you’re likely to die of?) to the cornucopia of questionable internet services offering intimate
information (do you want to know whose profiles your partner views, or how their past conquests rate them?)
the value of information is as hard to determine as information is easy to obtain.

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