from communication between individuals to collective beliefs frank van overwalle francis heylighen...
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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
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