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CSCI 534(Affective Computing) – Lecture by Jonathan Gratch
Experiment
▪ We going to play a game for some real $$
Reminder on Economic vs. Psychological researchDeception taboo in economic games
CSCI 534(Affective Computing) – Lecture by Jonathan Gratch
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
CSCI 534(Affective Computing) – Lecture by Jonathan Gratch
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
CSCI 534(Affective Computing) – Lecture by Jonathan Gratch
A strange game. The only winning move is not to play
- War Games
CSCI 534(Affective Computing) – Lecture by Jonathan Gratch
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
CSCI 534(Affective Computing) – Lecture by Jonathan Gratch
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)
CSCI 534(Affective Computing) – Lecture by Jonathan Gratch
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
CSCI 534(Affective Computing) – Lecture by Jonathan Gratch
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
CSCI 534(Affective Computing) – Lecture by Jonathan Gratch
Preview
Encoding
Emotion is evocative
AppraisalDesirability
Expectedness
Controllability
Causal Attribution
Emotion
Situation Goals
AppraisalDesirability
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
CSCI 534(Affective Computing) – Lecture by Jonathan Gratch
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)
CSCI 534(Affective Computing) – Lecture by Jonathan Gratch
Variants of Rational Choice Theory
▪ Decision theory centers on cost-benefit
calculations that individuals make without
reference to anyone else’s plans (Lecture 7)
CSCI 534(Affective Computing) – Lecture by Jonathan Gratch
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
CSCI 534(Affective Computing) – Lecture by Jonathan Gratch
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%
CSCI 534(Affective Computing) – Lecture by Jonathan Gratch
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
CSCI 534(Affective Computing) – Lecture by Jonathan Gratch
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
CSCI 534(Affective Computing) – Lecture by Jonathan Gratch
Game Theory ExamplesSend a signal
Assumption: my actions will influence
other’s actions
This is the essence of game theory
CSCI 534(Affective Computing) – Lecture by Jonathan Gratch
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
CSCI 534(Affective Computing) – Lecture by Jonathan Gratch
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);
CSCI 534(Affective Computing) – Lecture by Jonathan Gratch
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
CSCI 534(Affective Computing) – Lecture by Jonathan Gratch
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
CSCI 534(Affective Computing) – Lecture by Jonathan Gratch
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
CSCI 534(Affective Computing) – Lecture by Jonathan Gratch
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
CSCI 534(Affective Computing) – Lecture by Jonathan Gratch
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
CSCI 534(Affective Computing) – Lecture by Jonathan Gratch
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
CSCI 534(Affective Computing) – Lecture by Jonathan Gratch
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
CSCI 534(Affective Computing) – Lecture by Jonathan Gratch
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
CSCI 534(Affective Computing) – Lecture by Jonathan Gratch
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
CSCI 534(Affective Computing) – Lecture by Jonathan Gratch
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
CSCI 534(Affective Computing) – Lecture by Jonathan Gratch
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
CSCI 534(Affective Computing) – Lecture by Jonathan Gratch
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
CSCI 534(Affective Computing) – Lecture by Jonathan Gratch
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
CSCI 534(Affective Computing) – Lecture by Jonathan Gratch
One solution: Maybe talk isn’t cheap
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”
CSCI 534(Affective Computing) – Lecture by Jonathan Gratch
One solution: Maybe talk isn’t cheap
▪ 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.
CSCI 534(Affective Computing) – Lecture by Jonathan Gratch
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
CSCI 534(Affective Computing) – Lecture by Jonathan Gratch
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
CSCI 534(Affective Computing) – Lecture by Jonathan Gratch
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
CSCI 534(Affective Computing) – Lecture by Jonathan Gratch
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)
CSCI 534(Affective Computing) – Lecture by Jonathan Gratch
Preview
Encoding
Emotion is evocative
AppraisalDesirability
Expectedness
Controllability
Causal Attribution
Emotion
Situation Goals
AppraisalDesirability
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
Social Goals“Do unto others…”
CSCI 534(Affective Computing) – Lecture by Jonathan Gratch
First (today)
AppraisalDesirability
Expectedness
Controllability
Causal Attribution
Emotion
Situation Goals
ActionTendency
Social Goals“Do unto others…”
CSCI 534(Affective Computing) – Lecture by Jonathan Gratch
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)
CSCI 534(Affective Computing) – Lecture by Jonathan Gratch
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
CSCI 534(Affective Computing) – Lecture by Jonathan Gratch
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
CSCI 534(Affective Computing) – Lecture by Jonathan Gratch
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
CSCI 534(Affective Computing) – Lecture by Jonathan Gratch
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
CSCI 534(Affective Computing) – Lecture by Jonathan Gratch
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
CSCI 534(Affective Computing) – Lecture by Jonathan Gratch
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?
CSCI 534(Affective Computing) – Lecture by Jonathan Gratch
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
CSCI 534(Affective Computing) – Lecture by Jonathan Gratch
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
CSCI 534(Affective Computing) – Lecture by Jonathan Gratch
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 - $0) / 2
▪ Relies on beliefs about other player’s intentions– I believe; I believe that you believe
CSCI 534(Affective Computing) – Lecture by Jonathan Gratch
How do we form these beliefs?
▪ Playing randomly?
▪ Attending to my actions?
▪ Attending to my emotions?
▪ Care about farness?
▪ More generally, what is my opponent’s “type”
▪ How did you figure out?
CSCI 534(Affective Computing) – Lecture by Jonathan Gratch
General comments on other-regarding preferences
▪ People act as thought they care about others
▪ Can incorporate these into utility function
▪ Improves fit to data
▪ Also allows us to model individual “types”
– How does behavior change if we alter alpha and beta?
▪ Envy usually larger than guilt
Ume($me , $you) = $me – αme ∙ max{$you – $me ,0}
– βme ∙ max{$me – $you ,0}
CSCI 534(Affective Computing) – Lecture by Jonathan Gratch
Individual differences
▪ Social-value orientation– Some people more individualistic, some more collaborative
– Can model with alpha and beta
▪ Culture
– Some cultures more collectivist (e.g., China)
– More guilt for “in-group” members
– Less guilt toward “out-group members
Ume($me , $you) = $me – αme ∙ max{$you – $me ,0}
– βme ∙ max{$me – $you ,0}
CSCI 534(Affective Computing) – Lecture by Jonathan Gratch
Receiver
Situational differences: e.g., Ultimatum Game
62
Sender
Guilt
$5 $1
CSCI 534(Affective Computing) – Lecture by Jonathan Gratch63
Sender
$5 $1
Guilt
Situational differences: e.g., Ultimatum Game
CSCI 534(Affective Computing) – Lecture by Jonathan Gratch64
Sender
$5 $1
Guilt
Situational differences: e.g., Ultimatum Game
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)
CSCI 534(Affective Computing) – Lecture by Jonathan Gratch
Social goals
▪ Up to now we have focused on fairness
▪ Being unfair is a type of social harm
CSCI 534(Affective Computing) – Lecture by Jonathan Gratch
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
CSCI 534(Affective Computing) – Lecture by Jonathan Gratch
Mind perception explains “distance” effects
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 →
CSCI 534(Affective Computing) – Lecture by Jonathan Gratch
Mind perception explains “distance” effects
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
CSCI 534(Affective Computing) – Lecture by Jonathan Gratch
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
CSCI 534(Affective Computing) – Lecture by Jonathan Gratch
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
CSCI 534(Affective Computing) – Lecture by Jonathan Gratch
Social goals
▪ Up to now we have focused on fairness, harm
▪ Can you think of other social goals?
CSCI 534(Affective Computing) – Lecture by Jonathan Gratch
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
CSCI 534(Affective Computing) – Lecture by Jonathan Gratch
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
CSCI 534(Affective Computing) – Lecture by Jonathan Gratch
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