Lecture 6: Emotion and decision-making

CSCI 534(Affective Computing) – Lecture by Jonathan Gratch

Lecture 6: Emotion and decision-making


IF THE

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CHANGED


Lecture 6: Emotion and decision-making


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Should schools reopen?

Should I get vaccinated?

Should I wear a mask?

?


Can emotions make us smart? Example

Wilson, T. D., Lisle, D., Schooler, J. W., Hodges, S. D., Klaaren, K. J., & LaFleur, S. J.

(1993). Introspecting about reasons can reduce post-choice satisfaction. Personality and

Social Psychology Bulletin, 19, 331–339.

Task: Pick a poster to take home

• One group just grabbed one they

feel good about

• Other group asked to think carefully

and write down their reasons for

choosing

• Got to reconsider 6-months later


This lecture

▪ Emotion and decision-making

– Examine what emotions do: how they shape decisions

– Introduce “rational” models of decision-making

▪ Review rational choice theory (model that underlies economics)

– Contrast emotional from “rational” decisions

▪ Illustrate “emotional” departures from rational choice

▪ Describe “behavioral economic” models that capture this


First an experiment

▪ Need 3 volunteers (that consent to being filmed)

– Opportunity to win a reward

– Have a chance of winning up to $20

▪ Note on Economic vs. Psychological research– Deception heavily discouraged in economics (can’t publish)


Blue Player Red Player Gold Player

First count your pulse for 20 seconds

Write it down



Blue Player Red Player Gold Player

Finally, count your pulse for 20 seconds

Write it down


Which gamble would you prefer?

Gamble 1 Gamble 2


Another Illustration


$10

-$22

$10

-$54

$1

$5

$1

$9

In each case, expected value is $2

a) b)

c) d)


Emotion a benefit or curse? A very old debate

▪ Plato argues emotion and intellect are in opposition

Emotion

Reason

crooked lumbering animal, … the mate of

insolence and pride, shag-eared and

deaf, hardly yielding to whip and spur.”

lover of honor and modesty and

temperance, and the follower of true glory;

he needs no touch of the whip, but is

guided by word and admonition onlyThe Allegory of the Chariot


Emotion and Rationality

▪ Emotion is often said to “distort” reason. What

does that mean?

▪ It means there is a theory of how people “should”

make decisions (Rational Choice Theory)

– Follows from a set of principles that seem an irrefutable

characteristic of good decision-making

▪ And people don’t follow that theory

▪ And emotions help explain departures from

rational choice theory


Why should we care that people not “rational”

▪ Rational models are used to predict human decisions and

make policy decisions across wide range of applications

– Economic decisions: how individuals buy and invest

– Public policy decisions: how will programs impact the happiness and

well-being of populations

– Consumer choice: will consumers be satisfied by a product

– Market mechanisms design: will policies governing transactions in an

online marketplace be efficient

– Technology acceptance

▪ Rational models are used to guide automated systems

– Bargaining agents

– Security agents

– Navigation systems


Example: Security

Milind

Tambe

Rational models (game theory) can help us build

decision-aids for such efficient security resource

allocation. Use computational methods to predict

the decision-making of potential criminals


Example: Navigation

Sarit Kraus

Rational models can help us recommender systems such

as driver navigation systems. Challenge is to recommend

high quality routes that satisfy user preferences while

achieving other goals (energy efficiency)


Rational Choice Theory

▪ Developed over centuries

▪ Central foundation of economic decision-making

▪ Claimed to serve two basic purposes– Normative: how people (and machines) should act and think

▪ Helps us avoid confused, poor thinking

▪ Helps us analyze arguments

▪ Aids in design of “optimal” artificial decision-makers

– Descriptive: how people (and machines) actually act and think?▪ Fundamental postulate of economics: people act rationally

▪ Allows that individuals may not be rational but this can be viewed as noise so that the

population will act rationally (rationality “of the crowd”)


Variants of Rational Choice Theory

▪ Decision theory centers on cost-benefit

calculations that individuals make without

reference to anyone else’s plans

▪ Game theory analyzes how people make choices

based on what they expect other individuals to do.– We will discuss this when we consider social emotions


Why is decision theory called rational

▪ What makes a decision rational?

▪ How would we develop definition?


Axioms of Decision-Theory

1. Completeness:

– All actions (or objects) can be ranked in an order of preference;

indifference between two or more alternatives is possible

2. Transitivity: – If action (or object) a1 is preferred to action a2 and action a2 is preferred

to a3, then a1 is preferred to a3.

Tesla Model S BMW M6

AMC Pacer

=


Axioms of Decision-Theory

3. Continuity:

– When there are three lotteries (X, Y and Z), X preferred to Y and Y

preferred to Z, then there should be a mixture of X and Z such that an

individual is indifferent between this mix and Y

4. Independence:– If we mix two lotteries (X, Y) with a third one, the ordering of the two

mixtures will not change regardless of the particular third lottery used

90% + 10% = 100%

50% + 50% 50% + 50%

XY

Y Z


Decision Theory (or Expected Utility Theory)

▪ Decision theory satisfies these axioms

▪ Outcomes can be described by a utility function– The value (or happiness) derived from achieving this state

– E.G. Money could be a person’s measure of happiness

The value of winning a $1,000,000 lottery ticket is $1,000,000

▪ Outcomes can be described by a probability fn.– The likelihood that this state might be achieved in the future

▪ Decisions are then driven by Expected Utility


Decision Point

Chance Event

Decision Theory: people utility maximizers

20%

20%

80%

80% 100

0

70

70

U EU

100 * 80% + 0 * 20%= 80

70 * 80% + 70 * 20% = 70


Is Decision Theory “Rational”

▪ If decision maker doesn’t follow decision theory, always

possible to construct a choice of gambles such that they will

lose money


Utility Theory doesn’t assume money is people’s utility function

People assign utility to money. Different people have different utility fn.

Money not necessarily equal to happiness

Daniel Bernoulli 1738

Utility is

the “anticipated pleasure” of wealth

rather than wealth per se


Utility Theory doesn’t assume money is people’s utility function



Daniel Bernoulli 1738

CSCI 534(Affective Computing) – Lecture by Jonathan Gratch30

Utility Theory doesn’t require utility to equal objective value (i.e., money)


Monetary value

For many people, each dollar has less value

the richer we become


Utility theory assumes decisions are based

on subjective utility and subjective probability

Much research focuses on how to elicit these

decision parameters

Utilit

y

A dollar tomorrow is worth less than a dollar

today (temporal discounting)


Summary: Rational Choice Theory

▪ Decision theory centers on cost-benefit calculations that

individuals make without reference to anyone else’s plans

▪ Captures many aspects of how people make decisions

(maximize pleasure)

▪ Core assumption underling most economic theory and

economic decision

▪ Core assumption underlying most artificially intelligent

systems


Is Expected Utility a good model of human choice?



Test

<


Test

<


Gamble A Gamble B<


Gamble A Gamble B Gamble C Gamble D< >









89%

$1,000,000

89%

$1,000,000

11% < 11%


Violation of Axiom of Independence


89%

$1,000,000

89%

$1,000,000

89%

$0

89%

$0

11% < 11% 11% > 11%


Violation of Axiom of Independence

89%

$1,000,000

89%

$1,000,000

89%

$0

89%

$0

11% < 11% 11% > 11%


Another example

▪ Which would you rather do?

Drive to USC each day Live next to nuclear power plant

People overweight low probability but high (dis)utility events


Another example

• Fungibility is the property of a good or a commodity whose individual

units are essentially interchangeable

• An assumption in economics is money and commodities are fungible

• Endowment effect is violation of this assumption


Another example

2nd 3rd

• Reference dependent decision-making


What to do?

▪ People don’t follow axioms of decision theory

▪ Yet they are not random. Follow patterns

▪ Can we develop a descriptive utility theory?


What to do?




▪ Some models just say math is hard

Rank-dependent expected utilityPeople overweight low-probability events such as winning the lottery


What to do?




▪ Some models say people bad at math

▪ Many models appeal to concepts that seem like

emotion– Aversion, loss, regret, disappointment


One idea: emotions arising from riskPeople have different feelings toward risk. People can be risk avoiders, risk

seekers (or risk lover) , or indifferent toward risk (risk neutral).

Monetary

Value

Utility Value

Risk avoider

Risk lover

Risk neutral

Utility of money shown for different types of people. Note that for equal

increments in dollar value the utility either rises at a decreasing rate (avoider),

constant rate or increasing rate (lover).

Key point: Absolute value of outcome not important. It is feeling this evokes


One idea: counterfactuals

Prospect TheoryFeelings towards risk depend on a reference point

People fear losses more than the enjoy gains (w.r.t. this reference point)

KahnemanTversky


Modeling decisions: Big picture(Loewenstein and Lerner 2003)

How does emotion impact decision-making?


Modeling decisions: It’s messy(Loewenstein and Lerner 2003)


Biggest idea: Anticipated Affect

▪ Decisions impacted by how you anticipate you will

feel about the result

Decision/

behavior

Expected

consequences

Expected

emotions

David Hume

Claim: People will pick decisions with greatest expected pleasure


Anticipated Affect

▪ We’ll walk through one model in detail

▪ Mellers et al. Decision Affect Theory– Argues people anticipate how they will feel about outcomes of

decisions and use their predictions to guide choice

– People are assumed to choose option with greater subjective

expected pleasure

– Propose a mathematical model to predict how people feel

– Emphasizes role of specific emotions: surprise, disappointment, and

regret


$1

$5

$1

$5

Which of these outcomes would give you more pleasure?

Why?

Lottery A Lottery C

People find outcome of Lotter C more surprising and thus

are more elated

If both lotteries involved losses, the surprising outcome

would be more disappointing

Role of surprise


Decision Affect theory

▪ Models how we feel after an uncertain outcome

a Priori probability of

outcomeElated

Disappointed


$1

$5

Which of these outcomes would

give you more pleasure?

Why?

Lottery A

Lottery B

$-10

$5

This is another example of

counterfactual reasoning.

The availability of an

alternative influences

reactions

Our elation or disappointment

shaped by the difference

between obtained and

alternative outcome

Each outcome equally surprising

Role of relief and disappointment


Decision affect theory (DAT) – Part 1

▪ Consider a gamble with Outcomes A and B of utility UA and UB

respectively

▪ DAT predicts that the emotional response to A is

RA UA + d(UA - UB) (1 – pA)

– d(UA - UB), the disappointment function, is a power function with

different exponents for positive and negative distances

– In their data d(x) = x1.16 if x>0 and -|x|1.20

UB

UApA

Reward disappointment surprise


And this shapes choice between alternatives

▪ Consider a gamble with Outcomes A and B of utility UA and UB

respectively

▪ DAT predicts that the emotional response to A is

RA UA + d(UA - UB) (1 – pA)

▪ When choosing between gambles:

– Rather than using expected utility

– Use expected pleasure/pain

IF pARA + (1-pA)RB > pCRC + (1-pC)RD ,

Pick gamble 1, else pick gamble 2

Contrast EUT: IF pAUA+(1-pA)UB > pCUC+(1-pC)UD ,

Pick gamble 1, else pick gamble 2


ER = 8.75*1/4 – 0.32*.75 = 1.95

$10

-$54

$1

$5EU = $5*1/4 + $1*3/4 = $2

EU = $-54*1/8 + $10*7/8 = $2

RA = UA + d(UA - UB) (1 – pA)

5 + 41.16 * 0.75 = 8.75

RB = UB + d(UB - UA) (1 – pB)

1 + -(41.20)* .25 = -0.32

RC = UC + d(UC - UD) (1 – pC)

-54 + -(641.20)*7/8 = -182.7

ER = -182.7*1/8 + 25.6 * 7/8 = -.46

RD = UD + d(UD - UC) (1 – pD)

10 + 641.16 * 1/8 = 25.6

From Mellers, disappointment fn: d(x) = x1.16 if x>0 and -|x|1.20

A

B

C

D

25%

12.5%


But that’s not all! DAT Part 2

▪ Most decisions we only learn outcome of our choice

– What if I married someone else?

– What if I went to MIT instead of USC?

▪ We can’t go back in time and try things differently

– Mellers refers to this as partial feedback

▪ But sometimes we can

▪ Which of these bargains do you want to pick?

$-32

$30

$-8

$20

Under complete feedback we may experience regret

EU=-16.5 EU=-1


Regret

▪ Feelings under complete feedback modeled with regret

function

EA(C) UA + d(UA - UB) (1 – pA) + r(UA - UC) (1 – pA pC)

– d(UA - UB), the disappointment function

– r(UA - UC), the regret function, is a power function with different exponents for

positive and negative distances from alternative

Pick 1

reward disappointment surprise regret

Didn’t pick 2


But that’s not all! DAT Part 2

▪ Feelings under complete feedback modeled with regret

function

EA(C) UA + d(UA - UB) (1 – pA) + r(UA - UC) (1 – pA pC)

reward disappointment surprise regret

NOTE

These terms correspond to appraisal variables in appraisal

theory (desirability, expectedness, etc.)


DAT Summary

▪ Decisions shaped by surprise, disappointment and regret

Surprise

EffectsDisappointment

Effects

Regret

Effects

Pleasure

Displeasure


Summarize what we’ve learnedPeople try to maximize expected emotion (utility)

Decision/

behavior

Expected

consequences

Expected

emotions

David Hume


And expected emotions ≠ expected utility

▪ Expected emotions are shaped by uncertainty (risk) in ways

that violate independence assumption

– People overweigh small probabilities

– People underestimate large probabilities

– Losses Loom larger than gains

Su

bje

cti

ve

pro

ba

bil

ity Probability

function

Prospect TheoryKahneman & Tversky, 1979 Decision Affect Theory

Mellers,


This fits with what we learned from appraisal theory

RA(C) UA + d(UA - UB) (1 – pA) + r(UA - UC) (1 – pA pC)

Goals Anticipated Events

(outcomes)

Cognitive evaluation

(appraisal)

Affect Decision

Reward

Counterfactual

reasoning

expectation

SurpriseRegret

Disapp.

joy

A & C

(UA)


Appraisal models (review)

▪ Computational models of appraisal propose simple

rules that appraise abstract data structures

Whack BirdCause: self

Intend: yes

Prob.: 50%

SafeUtility: 50Prob: 50%

Intend: True

Past Present Future ➔

Safe

Utility: 50 Prob.: 100%Belief: False

Bird AttackCause: Other

Intend: yes

Prob: 100%

Inhibits


Summarize what we’ve learnedPeople try to maximize expected emotion

Decision/

behavior

Expected

consequences

Expected

emotions

David Hume

And if we forecast the emotions people anticipate from a decision,

we can recommend decisions that make them happy, yes?


Here’s a complication

Rate intensity of the following events (1-weak; 10-strong)

▪ Jack sustained fatal injuries in a car crash

▪ Jack was killed by a semi trailer that rolled over on his car and

crushed his skull

▪ Jack lost the skin of his

finger in a rugby match

▪ What’s going on here?


Here’s a complication

▪ We tend to feel stronger emotions when the stimuli is vivid

▪ When we imagine future situations we often fail to vividly

imagine the consequences


Problem: It seemed a good idea at the time…

The morning after effect


Affective forecasting

▪ People make decisions by forecasting their emotions

▪ People not so good at forecasting

– What: what emotion they will feel following a decision

– How much: the intensity of the experience

– How long: the duration of the emotion

▪ People reason about the future abstractly

– “Jack sustained fatal injuries “

▪ People fail to account for their ability to cope

– Become desensitized to positive circumstances

– Become resigned to negative circumstances

▪ People overweigh outcomes in immediate focus

– E.g., Students in mid-west predicted they would be happier moving to

California; students in California predicted they’d be less happy in mid-

west; yet both equally happy


As a consequenceExpected emotion ≠ experienced emotion

Decision/

behavior

Expected

consequences

Expected

emotions


Poor affective forecasting leads to poor decisions

▪ “morning after” effects

▪ Low retirement savings rates

▪ Lack of energy conservation

▪ Risky health choices

▪ Impulsivity


Affective forecasting

▪ Evidence for 2 distinct mechanisms for forecasting

– Simulation route:

▪ Vividly imagine being in a certain situation

▪ “read” our bodily reactions to that situation (Damasio’s somatic

marker hypothesis)

– Reasoning route:

▪ Reason about emotions: e.g., I expect I would feel this way

▪ Evidence that the “reasoning” approach more suspect to mis-

forecasting effects

– Situational factors bias these mechanisms

▪ E.g. more immediate events more likely to use simulation route

– Some individual differences predict this tendency

▪ Mental imagery ability (White 1978)


Attempts to salvage EUT

▪ Expected emotions are time dependent: care less about

events far in the future (explains procrastination?)

▪ Can be modeled with hyperbolic discounting

Make utility a

function of time Expected

Discounted

Cumulative

Reward

(Q-value)

Does this sound

familiar?


MacInnis. Whan. "Looking through the crystal ball: Affective forecasting and misforecasting in

consumer behavior." Review of Marketing Research 2 (2005): 43-80.


Could affective computing help


Approach: Make the consequences of decisions

immediate and tangible through virtual reality

(Bailenson)


Example 1: environmental conservation

▪ Global warming serious issue

▪ People tend to support conservation policies but

people often wasteful in their individual choices

– Could virtual reality make consequences seem more vivid?

– Would this result in actual pro-environmental behavior?


Study 1: waste

Pre-tested attitudes on conservation

Told # of trees cut down to make toilet paper

Virtual Reality Mental Imagery


Study 1: environmental conservation


Study 1: waste

Pre-tested attitudes on conservation

Told # of trees cut down to make toilet paper

Virtual Reality Mental Imagery

Irrelevant Task (30 minutes)

Cleanup spilt water

VR participants used significantly fewer napkins 30min later


Example 2: Shower study

▪ Could same idea get people to use less water?

– Read about coal and shower

– Touched physical coal

– Washed hands

– In VR randomly assigned to 1 of 4 conditions

– 6 minute virtual shower

– Washed hands (main DV)


Experimental Conditions





Vivid conditions yielded significantly quicker hand washing


Yet another example


Summary

Decision/

behavior

Expected

consequences

Expected

emotions

Expected emotions ≠ experienced emotions

But this difference can be predicted and possibly reduced


But that’s not all

recall experiment….


Example

Incidental

influences

Immediate

emotions


Incidental influences

Maximize

Expected

Utility

ProbabilityExpected

Emotion

Anticipatory

influences

▪ Unrelated events can influence our immediate

emotions

– Sunny day

– Happy or sad music

– Disgusting room

Incidental

influences

Immediate

emotions


Another example

▪ Use of affective computing technology to demonstrate the

pervasive impact of incidental influences


Another example



Analyzed the sentiment of posts using Linguistic Inquiry Word Count (LIWC), a widely used and validated word classification system


Another example



Analyzed the sentiment of posts using Linguistic Inquiry Word Count (LIWC), a widely used and validated word classification system


Results

▪ What happens when Facebook poster is in rainy city?

– More negative and less positive posts

– e.g., a rainy day in New York City directly yields an additional 1500 (95% CI

1100 to 2100) negative posts by users in New York City

▪ What happens when a facebook poster has friends in a rainy

city?

– They “catch” their friends emotions

– A rainy day in New York City yields about 700 (95% CI 600 to 800) negative

posts by their friends elsewhere


Finally: Immediate emotions change how we forecast(Loewenstein and Lerner 2003)


AngerLerner&Tiedens06:

Portrait of the angry

decision maker



EmotionAction

Tendencies“Affect”

PhysiologicalResponse

EnvironmentGoals/Beliefs/

Intentions

Appraisal Tendencies Framework (Han, Lerner, Keltner 2007.)

Desirability

Controllability

Causal Attribution

Emotion

Withdraw Sadness Lo Arousal


Intentions

UNDESIRABLE

UNCONTROLABLE

BLAMEWORTHY


EmotionAction




Intentions


Desirability

Controllability

Causal Attribution

Emotion

Withdraw Sadness Lo Arousal


Intentions

UNDESIRABLE

CONTROLABLE

BLAMEWORTHY

APPROACH ANGER Hi Arousal


EmotionAction




Intentions


Desirability

Controllability

Causal Attribution

Emotion

APPROACH ANGER Hi ArousalWithdraw Sadness Lo Arousal


Intentions

UNDESIRABLE

CONTROLABLE

BLAMEWORTHY

UNCONTROLABLE


Summary(Loewenstein and Lerner 2003)


Summary

▪ People don’t follow rational choice theory

▪ People’s emotions depend on comparisons with

other possible outcomes (counterfactuals)

▪ People make decisions based on expected emotion,

not expected utility

▪ But people are bad at predicting their actual

emotions

▪ And influenced by irrelevant emotion

▪ And this can be modeled computationally


Next Time

▪ Today we had a number of experiments

▪ Many class projects will have experiments– E.g., show evidence your ideas work

▪ Next time we’ll have guest lecture on experimental

design– Also see recommended reading

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Lecture 6: Emotion and decision-making

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