Lecture 14: Social Emotions

CSCI 534(Affective Computing) – Lecture by Jonathan Gratch

Lecture 14: Social Emotions


Experiment

▪ We going to play a game for some real $$

Reminder on Economic vs. Psychological researchDeception taboo in economic games


Game theory experimentThe dollar auction

1. Highest bidder wins $10

2. Bidding starts at $1 and proceeds in $1 increments. And, yes, this

is for real money.

3. I will give all bidders fair warning before the auction ends.

4. Cartels and collusion among bidders are strictly prohibited. This

means no communication, verbal or nonverbal, is allowed (other

than bidding)

5. The highest bidder pays me what they bid and receives $10.

6. The second highest bidder pays me what they bid.

7. Only Play if you prepared to pay me


What should have happened

▪ What happened and how can we explain it?– The structure of task can “hook” bidders that bid high

– E.g. to avoid a loss of $9, one can bid $11 and only lose $1 (if bidding stops)

▪ Why do people stop bidding?– When they realize they better cut their losses

– Unfortunately, hard to recognize this early in the game

▪ Why don’t people stop bidding?– People can get caught in “auction fever”: many factors conspire

▪ People tend to get excited when they bid

▪ Emotions increase when auction deadlines approach (Ku, Malhotra & Murnighan 2005)

▪ People want to avoid loss

▪ Task emphasizes importance of Theory of Mind– Important to anticipate how others will respond

– Important to shape other’s beliefs about you (e.g., I will never back down)

– This reasoning is recursive and thus difficult


A strange game. The only winning move is not to play

- War Games


Another game

▪ I give $10 to “Proposer” (P)

▪ Proposer can split with “Responder” (R): offer $X ∈ {$0 .. $10}

▪ R can accept or reject

▪ If R accepts, R gets $X, P gets $10-X, (e.g., P keeps $7, R keeps $3)

▪ If R rejects, R gets $0, P gets $10-X, (e.g., P keeps $7, R keeps 0)

▪ What offer X yields most $ to Proposer?

▪ What decision (accept/reject) yields most $ for Responder?

▪ Most people offer $2 or $3. Why?

▪ How much power does Responder have to influence proposer?

▪ People often reject unfair offers. Why?

Called the impunity game


Yet Another game

▪ I give $10 to “Proposer” (P)

▪ Proposer can offer $X ($0 to $10) to “Responder” (R)

▪ R can accept or reject

▪ If R accepts, R gets $X, P gets $10-X,

▪ If R rejects, both get $0

▪ What offer yields most $ to Proposer?

▪ What decision yields most $ for Responder?

▪ Most people offer $4 or $5 (more than last game). Why?

▪ How much power does Responder have over proposer?

Called the ultimatum game(take it or leave it)


How do Chimps play the Ultimatum Game?

▪ Chimpanzees behave according to rational analysis.

They propose an unequal split and it is not rejected

(Jensen, Call, Tomasello 2007)

Slide borrowed from Edward Cartwright


Overview: Emotions in social situations

▪ Preview next 3 lectures

▪ Introduce social rationality: – What is “proper” way to make social decisions?

– Game theory

▪ Highlight departures from classical game theory

▪ Discuss “behavioral game theory” – Considers how to incorporate emotional influences

– Discuss Fehr and Schmidt’s Equity Aversion Model


Decision-theory Reminder


Preview

Encoding

Emotion is evocative

AppraisalDesirability

Expectedness

Controllability

Causal Attribution

Emotion

Situation Goals


Expectedness

Controllability

Causal Attribution

Emotion

Situation Goals

Social Goals“Do unto others…”

Emotion is Social

Information

Emotion is

Evocative

Feedback

ActionTendency

Social decision-

making

DecodingSignal

Noise

Regulation and

Strategic Emotions

ActionTendency


Theories of Social Decision-Making


Rational Choice Theory (Review)

▪ Developed over centuries

▪ Central foundation of economic decision-making

▪ Serves 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)


Variants of Rational Choice Theory

▪ Decision theory centers on cost-benefit

calculations that individuals make without

reference to anyone else’s plans (Lecture 7)


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



What if agent playes “fixed policy”- Ignores your actions

- Choose Green 60% of time

- Choose Blue 40% of time

How should you play against such

a policy?

This is Decision Theory

Solve w/ reinforcement learning

Do you think your behavior

influenced the agent?- Emotions

- Decisions

S

$5Green $2

60% 40%

$7Blue $4

60% 40%


What if agent plays tit-for-tat- Green if you choose green on last

round

- Blue if you chose blue on last round

How should you play against such

a policy?

This is Game Theory

CANNOT solve via reinforcement

learning.

Need to think about opponent’s

responses to your actions

Do you think your behavior

influenced the agent?- Emotions

- Decisions


How did agent play?

2x2 mixed factorial design: strategy (within) x expression (between)

de Melo and Terada. The interplay of emotion expressions and strategy in promoting cooperation in

the iterated prisoner’s dilemma. Scientific Reports 2020


Game Theory ExamplesSend a signal

Assumption: my actions will influence

other’s actions

This is the essence of game theory


Example


Another approach

Imagine these are all driverless cars

Assumption: my actions cannot

influence other’s actions

These cars are just part of the

environment

This is the essence of decision-theory


FYI: game theory exercises different brain regions

▪ Compared to decision theory, people use different brain regions

– MPFC associated with Theory of Mind Reasoning

– Insula associated with emotion and activated when treated unfairly

▪ These regions not activated when playing same game against a

computer (people special)

Alan G. Sanfey, et al. Social Decision-Making: Insights from Game Theory and Neuroscience. Science

318, 598 (2007);


What is Game Theory?

▪ Game theory is a language for describing strategic interactions when what happens to one person is affected by another person

▪ A large number of situations that confront us in our day to day lives can be thought of as “games” with us as “players”

▪ And they can be analyzed using the tools of game theory

GT slides adapted from Ananish Chaudhuri, Department of Economics, University of Auckland


Games in everyday life

▪ Tennis players deciding whether to serve to the forehand or

backhand of their opponent

▪ The local bakery offering a discounted price on pastries just

before it closes

▪ Employees deciding how hard to work when the boss is away

▪ Pharmaceutical firms investing in research to develop a drug

▪ People bidding for stuff on eBay

▪ Airline companies trying to decide whether to cut prices


Pioneers of Game Theory

▪ Game theory enables us to understand and analyze the nature of the interaction between players in such games

▪ Foundations developed by von Neumann and Morgenstern

▪ Extended by John Nash (played by Russell Crowe in “A Beautiful Mind”) with Reinhard Selten and John Harsanyi

▪ Used extensively in computer science, economics, biology, sociology, political science, and all branches of business-related disciplines such as management and marketing


Elements of A Game

▪ Player:

Who is interacting? N={1,2,…,n}

▪ Actions/ Moves: What the players can do?

Action set :

▪ Payoff: What the players can get from the game

RAu i

n

ii → =1:

iiliii aaaA ,,, 21 =

Payoff determined by joint action


Strategy

▪ Strategy: complete plan of actions

▪ Mixed strategy: probability distribution over the

pure strategies

▪ Payoff: .2,1),,(),( 212121 == jii j ji aaussssu

=== =

1,0),,,,(1

21

i

i

l

j

ijijiliiiii sssssssS


An Example: Rock-paper-scissor

▪ Players: A and B

▪ Actions/ Moves:

{rock, scissor, paper}

▪ Payoff:

u1(rock, scissor) = 1

u2(rock, paper) = -1

▪ Mixed strategiess1=(1/3,1/3,1/3)

s2=(0,1/2,1/2)

u1(s1, s2) = 1/3(0·0+1/2·(-1)+1/2·1)+

1/3(0·1+1/2·0+1/2·(-1))+1/3(0·(-1)+1/2·1+1/2·0)

= 0

rock paper scissor

rock

paper

scissor

0,0

0,0

0,0

-1,1A

B

-1,1

-1,1

1,-1

1,-1

1,-1


What is the solution of the game?


Typical Assumptions

▪ Axiomatic assumptions on games

1. Assumes player is rationally self-interestedIn any given situation a decision-maker always chooses the action

which maximizes own self-interest (i.e., maximize expected utility).

2. Assumes opponent is rationally self-interested

3. Assumes perfect knowledge: players know structure of gameMoves, utilities, etc.

4. Assumes communication only through actions

Talk is “cheap” (since people can lie, no point listening to them)

5. Assumes nothing carries over to other games


Example: Prisoners’ Dilemma (Split-Steal)

You

Green Blue

Green

Blue

Action

payoffs

Your best

move

Imagine Opponent

picks Green

Imagine Opponent

picks Blue

Your best

move

Picking Blue is the dominant strategy: best regardless of what other player does

Opponent

You Opp You Opp

You Opp You Opp

12, 12 0, 18

18, 0 6, 6


Example: Prisoners’ Dilemma

You

Green Blue

Green

Blue

Rational

SolutionPicking Blue is the dominant strategy: best regardless of what other player does

Highest

Joint

return

And people typically do better than “rational” solution

Why? Opponent

12, 12 0, 18

18, 0 6, 6


Iterated game

▪ You played a multi-round game. Does this change this

reasoning?

▪ Assume finite horizon game (4 rounds)

– Using argument above, can prove you should pick Green (non-

cooperative) choice.

– Similarly, can prove opponent will pick this as well

– Working backwards (backward induction) can prove you should pick

Green on round 1

▪ If unknown horizon more complicated but, given reasonable

assumptions, same conclusion follows


Why do people beat the rational solution?

▪ Not always the case

– Sometimes rational actors perform better

– Depends on structure of game

▪ But clear that people depart from the rational model

▪ Thus, rational model poor choice for predicting human social

behavior, especially when situations evoke emotions

▪ To fix, models appeal to concepts that seem like emotion


Why don’t people follow game theory?

▪ Axiomatic assumptions on games

1. Assumes player is rationally self-interestedIn any given situation a decision-maker always chooses the action

which maximizes own self-interest (i.e., maximize expected utility).

2. Assumes opponent is rationally self-interested

3. Assumes perfect knowledge: players know structure of gameMoves, utilities, etc.

4. Assumes communication only through actions

Talk is “cheap” (because it people can lie)

5. Assumes nothing carries over to other games


One solution: Maybe talk isn’t cheap

You

Green Blue

Green

Blue

12, 12 0, 18

18, 0 6, 6

Opponent

If we can predict opponent next action from words or emotions, we

can do better (e.g., they have a “tell”)

You Opp You Opp

You Opp Opp

In terms of game theory, knowing opponent’s first move is a special case

called a “Stackelberg Game”

You



You

Green Blue

Green

Blue

12, 12 0, 18

18, 0 6, 6

Your best

move

We know in advance

Opponent will pick Green

We know in advance

opponent will pick Blue

Your best

move

Opponent

You Opp You Opp

You Opp You Opp

If we can predict opponent next action from words or emotions, we

can do better (e.g., they have a “tell”)

In terms of game theory, knowing opponent’s first move is a special case

called a “Stackelberg Game”







▪ Previous example shows some limits of our abilities, but

evidence that people can predict cooperation

▪ People interacted w/ partner for 5 min before playing

▪ Were better than chance at predicting who would cooperate

Brosig, J. (2002). Identifying cooperative behavior: some experimental results in a prisoner's dilemma

game. Journal of Economic Behavior and Organization, 47, 275-290.

Frank, R. H., Gilovich, T., & Regan, D. T. (1993). The evolution of one-shot cooperation: an experiment.

Ethology and Sociobiology, 14, 247-256.


Affective computing approach

Identified nonverbal cues in human

dyads that were associated with

untrustworthiness

If robot showed these cues before game, people didn’t trust it


One solution: relax assumption people purely self-interested

▪ What would you actually pick?

▪ Why?

▪ How would you feel?

You

Green Blue

Green

Blue

12, 12 0, 18

18, 0 6, 6

Your best

move

We know in advance

Opponent will pick Green

Opponent

You Opp You Opp

You Opp You Opp


One solution: relax assumption people purely self-interested

▪ Recall, can fix decision theory by maximizing expected

emotion rather than expected utility

▪ Maybe we have emotions about other people?

– We feel bad when we hurt others

▪ Feel guilt

▪ Try to repair relationships

– We feel bad when other’s hurt us

▪ Feel anger

▪ Try to get even


Another solution: We are influenced by other’s emotion

▪ Emotional signals reinforce prosocial motives

– We feel bad when we hurt others (feel guilt)

– We may feel worse if they show they are hurt (show anger)


Preview

Encoding

Emotion is evocative


Expectedness

Controllability

Causal Attribution

Emotion

Situation Goals


Expectedness

Controllability

Causal Attribution

Emotion

Situation Goals

Emotion is Social

Information

Emotion is

Evocative

Feedback

ActionTendency

Social decision-

making

DecodingSignal

Noise

Regulation and

Strategic Emotions

ActionTendency



First (today)


Expectedness

Controllability

Causal Attribution

Emotion

Situation Goals

ActionTendency



Social Goals

▪ Majority of economic and game-theoretical models based on

the assumption that agents have self-regarding preferences

▪ But people don’t only care about themselves

– We feel bad when we hurt others (guilt)

– Wee feel bad when others hurt us (anger)


Examples of “other-regard”?

▪ Donating to charity

▪ Opening a door for someone carrying a heavy item

▪ Yielding to somebody who is trying to merge into rush

hour traffic

▪ An eBay seller providing positive feedback on a buyer

after the buyer provides positive feedback on the seller

▪ Repaying a favor


Examples of other-regarding behavior?


Examples of other-regarding behavior?

▪ This personality difference called Social Value Orientation (SVO)

▪ It’s an example of an other-regarding preference

▪ Are all other-regarding preferences pro-social?

▪ And, actually, this not inconsistent with rational theory

– Axioms of decision theory don’t say utility is self-interested

“Rational”

choice

“Rational”

choice

Altruist Self-interestedFair

Competitive


One model: Inequity Aversion (Fehr & Schmidt, 2006)

▪ Self-interest only considers our own outcomes– When receiving offer in ultimatum game, $1 better than $0

▪ Fairness involves a social comparison– Hey! You got $4!

– We feel bad when others gain more than us (envy)

– We feel bad when we gain more compared with others (guilt)

▪ Just like Decision-affect, theory, we can change the utility fn

Ume($me , $you) = $me – αme ∙ max{$you – $me ,0}

– βme ∙ max{$me – $you ,0}

Self interest

Envy

Guilt


Receiver

Example: Ultimatum Game

53

Envy

Sender

Receiver’s

Perspective

$5 $1

U($1,$4) = $1 – α×max{4 – 1, 0} - β×max{1-4,0}

U($1,$4) = $1 – α×3 - β×0

U($1,$4) = $1 –3 = -$2 (if α=1); Receiver will reject

α = Envy parameter

β = Guilt parameter


Receiver

Example: Ultimatum Game

54

Sender

Guilt

$5 $1

U($4,$1) = $4 – α×max{1 – 4, 0} - β×max{4 - 1,0}

U($4,$1) = $4 – α×0 - β×3

Sender’s

Perspective

U($4,$1) = $4 – 3 = $1 (if β=1);

Sender will make offer (but feel guilty about it)

α = Envy parameter

β = Guilt parameter


Limitations of Inequity Aversion

▪ Emphasizes relative fairness of outcomes

– If outcome is unequal across multiple parties, seen as unfair

▪ Is outcome the only factor people care about in

social situations?


Imagine this modifiedultimatum game

▪ I give $10 to Proposer

▪ Proposer can share some money with Responder

▪ Responder can accept or reject

▪ If Responder rejects, both get nothing (e.g., Ultimatum game)

▪ What if proposer gives $2?

▪ What if you learned that Proposer was only allowed to share

$0 or $2?

Inequity aversion

predicts reject


Intentions matter

▪ What matters is how the other person has treated me relative

to how they could have treated me

– People are willing to sacrifice their own payoff to help those that they think have

been kind to them

– The are prepared to give up their own payoff to punish those that they think

have been unkind


Rabin’s Reciprocity Model

▪ Emphasizes “kindness” over outcomes

Ui(ai, bj, ci) = πi(ai, bj) + α fj(ai, ci)[1- fi(ai, bj)]

– ai : player i's strategy (e.g., split or steal)

– bj : player i's belief about what player j's strategy will be

– ci : player i's beliefs about player j's beliefs about player i's strategy

– πi(ai, bj): player i’s payoff if I plays ai and j plays bj

– fi(ai, bj): player i’s “kindness”

Based on what player could give

= ($2 - $0) / 2

▪ Relies on beliefs about other player’s intentions– I believe; I believe that you believe


How do we form these beliefs?

▪ Playing randomly?

▪ Attending to my actions?

▪ Attending to my emotions?

▪ Care about farness?

▪ More generally, what is my opponent’s “type”

▪ How did you figure out?


General comments on other-regarding preferences

▪ People act as thought they care about others

▪ Can incorporate these into utility function

▪ Improves fit to data

▪ Also allows us to model individual “types”

– How does behavior change if we alter alpha and beta?

▪ Envy usually larger than guilt




Individual differences

▪ Social-value orientation– Some people more individualistic, some more collaborative

– Can model with alpha and beta

▪ Culture

– Some cultures more collectivist (e.g., China)

– More guilt for “in-group” members

– Less guilt toward “out-group members




Receiver

Situational differences: e.g., Ultimatum Game

62

Sender

Guilt

$5 $1

CSCI 534(Affective Computing) – Lecture by Jonathan Gratch63

Sender

$5 $1

Guilt


CSCI 534(Affective Computing) – Lecture by Jonathan Gratch64

Sender

$5 $1

Guilt


Dictator game with another participant “like you”

With machine representing another participant

With machine representing experimenter

With machine itself

Throw your $ in trash

Increasing social distance from “other”

People show less other-regard as

“social distance” increases

de Melo, Carnevale, and Gratch. Mind Perception of Computers and Humans (in prep)

Psychological Distance (Trope and Lieberman, 2010)


Social goals

▪ Up to now we have focused on fairness

▪ Being unfair is a type of social harm


Social harm


Another example


Mind perception explains “distance” effects

▪ Research suggests social cognition influenced by “mind perception”

▪ How we treat other entities depends on extent to which we attribute them “a mind”

▪ People organize other minds in 2 broad dimensions: Do they think? Do they feel?

Automation

Autonomy Humans

Animals



de Melo, Carnevale and Gratch, Social categorization and cooperation with autonomous agents and avatars, in prep.

Accounta

bili

ty →

▪ These mind perceptions have consequences

• Don’t feel envy

• Accept unfair offer

When treated unfairly

• Feel envy

• Reject unfair offerIn

tentionalit

y →



de Melo, Carnevale and Gratch, Social categorization and cooperation with autonomous agents and avatars, in prep.

Accounta

bili

ty →

Merits protection from harm→

When treating others unfairly

• Feel guilty

• Avoid causing harm

• Feel no guilt

• Happily cause harm


Also explains how we “dehumanize others”A

cco

un

tab

ility

→

Merits protection from harm→

▪ Animalistic dehumanization– Treat others “as though” they were

animals (deny them intelligence)

– Often done to minorities, colonialist

attitudes (i.e., patronizing)

▪ Mechanistic dehumanization– Treat others “as though” they were

machines or objects

– e.g., doctors often dehumanize

patients in this way


But emotion can move this around

How we treat other entities depends on extent to which we attribute them “a mind”

People organize other minds in 2 broad dimensions: Do they think? Do they feel?

Autonomous Agent Avatar

▪ Machines that express emotion treated “as if” they are human-controlled

Add behaviors that

Convey emotionTake emotions away

from the human

▪ Humans that fail to express emotion treated “as if” they are computer-controlled

74

For example

▪ People play as sender in iterated ultimatum game for $

▪ With a purported human or computer opponent

▪ That does or does not exhibit emotions in response to offers

de Melo, Carnevale, and Gratch. Mind Perception of Computers and

Humans (in prep)

▪ Suggests important role of “emotion-like” behaviors in

human-machine interaction

People more fair

towards other humans

But effect vanishes if we

control for emotion


Social goals

▪ Up to now we have focused on fairness, harm

▪ Can you think of other social goals?


Subjective value inventory

▪ Feelings about the instrumental outcome– How satisfied are you with your own outcome – i.e., the extent to

which the agreement benefited you

▪ Feelings about the self– Did you “lose face” (i.e., damage your sense of pride)

▪ Feelings about the process– Would you characterize the negotiation process as fair?

▪ Feelings about the relationship– How satisfied are you with your relationship with your counterpart(s)

as a result of this negotiation

Curhan, J. R., Elfenbein, H. A., & Xu, A. (2006). What do people care about when they negotiate? Mapping the

domain of subjective value in negotiation. Journal of Personality and Social Psychology, 91, 493–512


Moral foundation theory (Haidt)


A note on learning

▪ People (and algorithms) adapt to other’s behavior

▪ Different types of algorithms

– Focus only on own actions: Reinforcement learning

– Focus on other player’s strategies: Belief Learning▪ Fictious Play

▪ Cournot Adjustment

▪ Experienced Weighted Attractions learning

https://www.uni-heidelberg.de/md/awi/forschung/lecture_belief_based_learning.pdf

https://www.uni-heidelberg.de/md/awi/forschung/lecture_belief_based_learning.pdf


Summary

▪ Game theory describes how people “should” act in social

situations (proscriptive theory)

▪ People fail to follow predictions from game theory

▪ One solution is to model social goals (e.g., other-regarding

preferences)

▪ People vary in terms of other regard

– Based on individual differences (SVO)

– Based on culture

– Based on the situation and nature of their partner

– Based on “psychological distance” (animalistic and mechanistic

dehuminazation)

– Based on moral framework

▪ Technology can influence these processes


Documents

Lecture 14: Social Emotions