From Communication betweenFrom Communication between

Individuals to Collective BeliefsIndividuals to Collective Beliefs

Frank Van OverwalleFrancis Heylighen

Margeret Heath

Aim: Multi-Agent ModelAim: Multi-Agent Model

• Agents are separate entities that react on their own

• ...have their own cognitive representation and information processing

• …communicate with each other (local transmission of information)

• ...reaction is accumulation of prior history and recent information (local processing of information)

Aim: Multi-Agent ModelAim: Multi-Agent Model

A connectionist model of collective cognition and biases

• Use standard connectionist principles

to describe information processing within a single agent / individual

• Extend connectionist principles

to describe information processing between multiple agents / individuals

ConnectionismConnectionism

Analogy with human brain:

• Connections between units within agent

• Activation flows through connections between units

Internal Activation

Synapse = Connection

Weight

External Activation

External Activation

Neuron = Unit

Advantages of Connectionist ModelsAdvantages of Connectionist Models

• applying beliefs by automatic activation spread through target attribute connections

• forming and changing beliefs by modifying target attribute connections

• computations are fast: in parallel by simple and highly interconnected units

• computations are unconscious: without need for a central executive

Recurrent Architecture: Recurrent Architecture: Flow of ActivationFlow of Activation

External activatio

n

Output activation

Internal activation

Flow of ActivationFlow of Activation

External activatio

n

Internal activation

Jamayans

Honest

Smart

Weight ChangeWeight Change

Network tries to match the external and internal activation (external and internal view of the world)

• If the internal activation underestimates the external activation: increase weights

• If the internal activation overestimates the external activation: decrease weights

Weight ChangeWeight Change

External activatio

n

Internal activation

Jamayans

Honest

Smart

Weight Change: to match internal

with external

activation

If external activation is under-

estimated:increase weight

If external activation is over-

estimated:decrease weight

Delta Learning AlgorithmDelta Learning Algorithm

0.0

0.2

0.4

0.6

0.8

1.0

0 1 2 3 4 5 6 7 8 9

Trials

Honest

.20

.36

AdvantagesAdvantages

• Local processes (error & weight correction)• No central executive• Automatic & Little consciousness• Efficient & fast (parallel)

• Integration of• Novel information (external activation)• Short term memory (internal activation)• Long term memory / prior knowledge (weights)

CommunicationCommunication

CommunicationCommunication

Analogy with connectionist model:

• “Trust” connections between units of agents

• Communication flows by means of “trust“ connections between agents

Multi-Agent Model: Activation FlowMulti-Agent Model: Activation Flow

Agent 1Talking

Jamayans

Honest

Smart

Agent 2Listening

Jamayans

Honest

Smart

trust weights

Multi-Agent Model: Weight ChangeMulti-Agent Model: Weight Change

Tries to match the talking and listening activation (external and internal views of the world)

• When the activation received from the talking agent fits with internal beliefs of the listener: increase trust weights

• When the activation received from the talking agents does not fit with internal beliefs of the listener: decrease trust weights

Multi-Agent Model: Weight ChangeMulti-Agent Model: Weight Change

Agent 1Talking

Jamayans

Honest

Smart

Agent 2Listening

Jamayans

Honest

Smart

Weight Change: to match internal

with external

activation

If internal activation is similar:increase

trust weight

If internal activation

is different:decrease

trust weight

Multi-Agent Model: Weight ChangeMulti-Agent Model: Weight Change

Agent 1 Talking

Jamayans

Honest

Smart

Agent 2 Listening

Jamayans

Honest

Smart

.50

.36.36

.50

.60.36

.18

1.0

.50

.18

Because internal

and external

activation are

similar:increase

trust

Role of trust weightsRole of trust weights

Talking agent

Listening agent

determines how much the listener is sensitive to

the sent information:Grice’s maxim of quality

?

“do not say what is false”

Multi-Agent ModelMulti-Agent Model

Agent 2 Talking

Jamayans

Honest

Smart

Agent 1 Listening

Jamayans

Honest

Smart

Multi-Agent ModelMulti-Agent Model

Talking Agent 2

now Listening

Listening Agent 1 now Talking

Jamayans

Honest

Smart

Jamayans

Honest

Smart

If receiving trust

weightis high:

If receiving trust

weightis low: do the

opposite

boost activation (talk more on novel

info)

attenuate activation

(talk less on known info)

attenuate activation

(talk less on known info)

boost activation (talk more on novel

info)

Multi-Agent Model: Trust ChangeMulti-Agent Model: Trust Change

Agent 1 Talking

Jamayans

Honest

Smart

Agent 2 Listening

Jamayans

Honest

Smart

.36

.36

.80

Because receiving trust weight = +.30

> resting trust .50

(knows already)

1-.30 = 70%

activation spread to listener

.25

.50.50

1+.50 = 1.50%

activation spread to listener

Because receiving trust weight = -.50

< resting trust .50

(does not know).00

.80

Role of trust weightsRole of trust weights

Talking agent

Listening agent

determines how much novelty in the

information is expressed by the talker:

Grice’s maxim of quantity

determines how much the listener is sensitive to

the sent information:Grice’s maxim of quality

“do not say what is false”

“be as informativ

e as is required”

ApplicationsApplications

Maxim of Quality how sensitive are you to (trust) the speaker ?

Maxim of Quantity how much novel information do you tell the listener ?

ParametersParameters

Learning Rate = .30 how quickly do agents change their own beliefs ?

Trust Change Rate = .40 how quickly do agents change their trust in other agents’ utterances ?

Trust Tolerance =.50 how much error between utterances and own beliefs is tolerated ?

Resting Trust = .40 with how much trust do agents start ?

Maxim of QualityMaxim of Quality

Talking agent

Listening agent

determines how much the listener is sensitive to the

sending information

PersuasionPersuasion

Listener hears about arguments to take risky choice

attitude shifts towards arguments given

Ebbesen & Bowers (1974)Ebbesen & Bowers (1974)

Attitu

de

Sh

ift

10% 30% 50% 70% 90%

Argum ents H eard

-1 .0

-0 .5

0.0

0.5

1.0

Talking Agent Listening Agent ________________________________ _________________________________

Topic Arg1 Arg2 Arg3 Arg4 Topic Arg1 Arg2 Arg3 Arg4

Prior Learning of Arguments

#10 1 1 1 1 1

Talking

#1- 3 - 5 - 7 - 9 1 i i i i ? ? ? ? ?

Test

of Listener 1 ? ? ? ?

forming topic-argument

associations

Expressing internal “i”

beliefs

Hearing with “little ears”

Reading off resulting activation to test topic-feature

associations

Ebbesen & Bowers (1974)Ebbesen & Bowers (1974)

Attitu

de

Sh

ift

10% 30% 50% 70% 90%

Argum ents H eard

-1 .0

-0 .5

0.0

0.5

1.0

PersuasionPersuasion

Listeners are

• Not convinced by arguments of an outgroup (they do not trust these)

• More convinced by arguments of an ingroup (they trust these)

Mackie & Cooper (1984)Mackie & Cooper (1984)

Source

Attitu

de

Sh

ift

Ing roup Outgroup-10

-5

0

5 Pro Arguments Anti A rguments

persuaded by

pro / anti arguments

not persuaded

Talking Agent Listening Agent ________________________________ _________________________________

Topic Arg1 Arg2 Arg3 Arg4 Topic Arg1 Arg2 Arg3 Arg4

Setting Agent Listener trust to+1 for ingroup 0 for outgroup

Prior Learning of Pro (Anti) Arguments

#10 1 1 (-1) 1 (-1) 1 (-1) 1 (-1)

Talking

#10 1 i i i i ? ? ? ? ?

Test

of Listener 1 ? ? ? ?

Mackie & Cooper (1984)Mackie & Cooper (1984)

Source

Attitu

de

Sh

ift

Ing roup Outgroup-10

-5

0

5 Pro Anti S imulation

Referencing ParadigmReferencing Paradigm

Communication about bizarre image

• “Director” explains what the image looks like

• “Matcher” has to guess which of many images is being addressed

Development of common “ground”

Referencing ParadigmReferencing Paradigm

Referencing ParadigmReferencing Paradigm

T rial

Nu

mb

er o

f Wo

rds / Im

ag

e

1 2 3 4 5 60

10

20

30

40

50

60

70

D irector M atcher

Talking Agent (“Director”) Listening Agent (“Matcher”) ________________________________ _________________________________

# of Trials Image Martini Glass Legs Each Side Image Martini Glass Legs Each Side

Setting “Director” “Matcher” trust to +1 Setting “Director” “Matcher” trust to 0

Prior Observation of Image by “Director”

#10 1 1 1 .5 .5

Talking and Listening

#4 first + 4 1 i i i i ? ? ? ? ?#2 ? ? ? ? ? 1 i i i i

Test

of “Director” 1 ? ? ? ?of “Matcher” 1 ? ? ? ?

repeated expression condenses

information: strong/weak features

are polarized

“Director” talks more so that features are

stronger

Schober & Clark (1989)Schober & Clark (1989)

T rial

Nu

mb

er o

f Wo

rds / Im

ag

e

1 2 3 4 5 60

10

20

30

40

50

60

70

D irector M atcher

Schober & Clark (1989)Schober & Clark (1989)

Accu

racy (P

erce

nt C

orre

ct)

M atcher Overhearer Late Overhearer70

80

90

100

Unique Information & Free DiscussionUnique Information & Free Discussion

Information sampling is biased, so that Shared information is communicated sooner and more often than Unshared information

Larson et al. (1996)Larson et al. (1996)

D iscussion Position

Pe

rcen

t Me

ntio

ne

d S

ha

red

Info

rma

tion

2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 360.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

more unshared information is communicated

in the end

Talking Agent Listening Agent ___________________________________ ___________________________________

# of Trials Patient Shared1 Shared2 Unique1 Unique2 Patient Shared1 Shared2 Unique1 Unique2

Prior Learning

#10 1 1 1 1 0 #10 1 1 1 0 1

Talking and Listening

Shared 1 i i ? ? ?? ? ? 1 i i

Unique 1 i i ? ? ?? ? ? 1 i i

Test

1 ? ? ? ? 1 ? ? ? ?

shared features are already known and have little effect on

listeners

unique features are not known and have

more effect on listeners and thus on

whole group

however, more talk because group knows

more about them

Larson et al. (1996)Larson et al. (1996)

D iscussion Position

Pe

rcen

t Sh

are

d In

form

atio

n

2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 360.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

more unshared information is communicated

in the end

Gossip ParadigmGossip Paradigm Lyons Lyons & Kashima (2003) & Kashima (2003)

Sequential Communication of information about Jamayans

• Sharing of background information by 4 participants

Actual Shared (all stereotype consistent = SC)

>< Actual Unshared (SC + SI + SC + SI)

• New mixed story (SC + SI) told in a serial chain

Actua l Shared

Reproduc tion Pos ition

Pro

po

rtion

of S

tory E

lem

en

ts

1 2 3 40 .2

0 .3

0 .4

0 .5

0 .6

0 .7

0 .8

0 .9

1 .0

S C S i

Actua l Unshared

Reproduc tion Pos ition1 2 3 4

0 .2

0 .3

0 .4

0 .5

0 .6

0 .7

0 .8

0 .9

1 .0

Less spreading

of SI elements

More spreading

of SI elements

Talking Agent Listening Agent ________________________________ ______________________________

Jamayan Smart Stupid Honest Liar Jamayan Smart Stupid Honest Liar

Prior SC (SI) Information on Jamayans: Each Agent#10 smart 1 1 #10 honest 1 1 #10 stupid 1 1 #10 liar 1 1

Mixed (SC + SI) Story to Agent 1#5 smart 1 1 #5 liar 1 1

Talking and Listening#5 intelligence 1 i i ? ? ? #5 honesty 1 i i ? ? ?

Test: Each Agentsmart 1 ?stupid 1 ?honest 1 ?liar 1 ?

Actua l Shared

Reproduc tion Pos ition

Pro

po

rtion

of S

tory E

lem

en

ts

1 2 3 40 .2

0 .3

0 .4

0 .5

0 .6

0 .7

0 .8

0 .9

1 .0

S C S I S im ulation

Actua l Unshared

Reproduc tion Pos ition1 2 3 4

0 .2

0 .3

0 .4

0 .5

0 .6

0 .7

0 .8

0 .9

1 .0

S C S I S im ulation

Maxim of Quantity (Novelty)Maxim of Quantity (Novelty)

Talking agent

Listening agent

determines how much novelty in the

information is expressed by the talker

Multi-Agent ModelMulti-Agent Model

Agent 2Listening

Agent 1 Talking

Jamayans

Honest

Smart

Jamayans

Honest

Smart

If receiving trust

weightis high:

If receiving trust

weightis low: do the

opposite

boost activation (talk more on novel

info)

attenuate activation

(talk less on known info)

attenuate activation

(talk less on known info)

boost activation (talk more on novel

info)

Gossip Paradigm:Gossip Paradigm:LyonsLyons & Kashima (2003) & Kashima (2003)

• Perceived Sharing of background information by 4 participants

Knowledge (same information)>< Ignorance (different information)

Lyons & Kashima (2003)Lyons & Kashima (2003)

Perceived Sharedness

Pro

po

rtion

of S

tory E

lem

en

ts

Knowledge Ignorance0.2

0.3

0.4

0.5

0.6

0.7

0.8

SC S I

More spreading

of SI elements

Less spreading

of SI elements

Talking Agent Listening Agent ________________________________ ______________________________

Jamayan Smart Stupid Honest Liar Jamayan Smart Stupid Honest Liar

Setting Talking Listener trust weights to .20 above resting trust for Shared (> less Novelty)

.20 under resting trust for Unshared (> more Novelty)

Prior SC (SI) Information on Jamayans: Each Agent#10 1 1 #10 1 1 #10 1 1 #10 1 1

Mixed (SC + SI) Story to Agent 1#5 1 1 #5 1 1

Talking and Listening#5 1 i i ? ? ? #5 1 i i ? ? ?

Test: Each AgentSmart 1 ?Stupid 1 ?Honest 1 ?Liar 1 ?

Lyons & Kashima (2003)Lyons & Kashima (2003)

Perceived Sharedness

Pro

po

rtion

of S

tory E

lem

en

ts

Knowledge Ignorance0.2

0.3

0.4

0.5

0.6

0.7

0.8

SC S I S imulation

Gossip Paradigm:Gossip Paradigm:Clark (2004)Clark (2004)

Contrary to Lyons & Kashima’s (2003) participants who received – SC background information – mixed SC-SI story

Clark’s participants received – mixed SC+SI background information – SC story

Perceived Sharedness

Pro

po

rtion

of S

tory E

lem

en

ts

Knowledge Ignorance3

4

5

6

7

SC S I

Clark (2004)Clark (2004)

More spreading of SC elements (“grounding”

)

Clark (2004)Clark (2004)

Perceived Sharedness

Pro

po

rtion

of S

tory E

lem

en

ts

Knowledge Ignorance3

4

5

6

7

SC S I S imulation

Implications (take home lesson)Implications (take home lesson)

ImplicationsImplications

Trust is a basic feature of communication

(core human motive: S. Fiske, 2004)

• by: – me – other

• developed:– expected knowledge (e.g., doctor, ingroup)

(set beforehand by modeler)– developed automatically

ImplicationsImplications

• People “trust” what is similar to them

• … people can trust false information

• … especially when they belong to a group (of talkers) that is very isolated and their beliefs are seldom disconfirmed

• … other independent information to tell false from true is personal observation

ImplicationsImplications

How can unique / unbiased information be spread more ?

• iteration of utterances “ndaba”– spreading of unfamiliar / unique information

>< condensing in talker’s system: weak features die out

• resist temptation to ignore unique information– be explicit– be complete

e.g., machines are naive and indulgent, and give no FB

>< thinking robots

Unresolved QuestionsUnresolved Questions

Role of trust weightsRole of trust weights

Talking agent

Listening agent

Maxim of quality (sensitivity):

Presumably unconscious

Maxim of quantity (novelty):

Unconscious ?

Role of symbolic languageRole of symbolic language

Talking agent

Listening agent

Transformation of information in symbolic

format (speech): does this influences its spreading?

Role of politenessRole of politeness

Talking agent

Listening agent

Maxim of quantity (novelty):>< we sometimes tell things

the listeners likes to hear (e.g., tell more nice than bad things

about beloved boyfriend)

Role of network structuresRole of network structures

• Can the multi-agent network system develop efficient communication channels between agents ?

Role of leadershipRole of leadership

Leadership depends on

• trust

• social network

• central information exchange

>< may lead to biased information spreading

Thank youThank you