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From Communication between Individuals to Collective Beliefs Frank Van Overwalle Francis Heylighen Margeret Heath.

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Presentation on theme: "From Communication between Individuals to Collective Beliefs Frank Van Overwalle Francis Heylighen Margeret Heath."— Presentation transcript:

1 From Communication between Individuals to Collective Beliefs Frank Van Overwalle Francis Heylighen Margeret Heath

2 Aim: 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)

3 Aim: 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

4 Connectionism Analogy with human brain: Connections between units within agent Activation flows through connections between units

5 Internal Activation Synapse = Connection Weight External Activation  Neuron = Unit

6 Advantages 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

7 Recurrent Architecture: Flow of Activation External activation Output activation Internal activation

8 Flow of Activation External activation Internal activation Jamayans Honest Smart

9 Weight 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

10 Weight Change External activation 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

11 Weight Change: Example (step 1) External activation Internal activation Jamayans Honest Weight Change: to match internal (.00) with external (1.00) activation Because external activation is under- estimated: increase weight

12 Weight Change: Example (step 2) External activation Internal activation Jamayans Honest Because external activation is under- estimated: increase weight

13 Delta Learning Algorithm.20.36

14 Advantages 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)

15 Communication

16 Communication Analogy with connectionist model: “Trust” connections between units of agents Communication flows by means of “trust“ connections between agents

17 Multi-Agent Model: Activation Flow Agent 1 Talking Jamayans Honest Smart Agent 2 Listening Jamayans Honest Smart trust weights

18 Multi-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

19 Multi-Agent Model: Weight Change Agent 1 Talking Jamayans Honest Smart Agent 2 Listening 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

20 Multi-Agent Model: Weight Change Agent 1 Talking Jamayans Honest Smart Agent 2 Listening Jamayans Honest Smart Because internal and external activation are similar: increase trust

21 Role 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”

22 Multi-Agent Model Agent 2 Talking Jamayans Honest Smart Agent 1 Listening Jamayans Honest Smart

23 Multi-Agent Model Talking Agent 2 now Listening Listening Agent 1 now Talking Jamayans Honest Smart Jamayans Honest Smart If receiving trust weight is high: If receiving trust weight is low: do the opposite boost activation (talk more on novel info) attenuate activation (talk less on known info) boost activation (talk more on novel info)

24 Multi-Agent Model: Trust Change Agent 1 Talking Jamayans Honest Smart Agent 2 Listening Jamayans Honest Smart Because receiving trust weight = +.30 > resting trust.50 (knows already) = 70% activation spread to listener = 1.50% activation spread to listener Because receiving trust weight = -.50 < resting trust.50 (does not know)

25 Role 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 informative as is required”

26 Applications Maxim of Qualityhow sensitive are you to (trust) the speaker ? Maxim of Quantityhow much novel information do you tell the listener ?

27 Parameters Learning Rate =.30how quickly do agents change their own beliefs ? Trust Change Rate =.40how quickly do agents change their trust in other agents’ utterances ? Trust Tolerance =.50how much error between utterances and own beliefs is tolerated ? Resting Trust =.40with how much trust do agents start ?

28 Maxim of Quality Talking agent Listening agent determines how much the listener is sensitive to the sending information

29 Persuasion Listener hears about arguments to take risky choice attitude shifts towards arguments given

30 Ebbesen & Bowers (1974)

31 Talking Agent Listening Agent _________________________________________________________________ TopicArg1Arg2 Arg3 Arg4 Prior Learning of Arguments # Talking # i iii???? ? 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

32 Ebbesen & Bowers (1974)

33 Persuasion Listeners are Not convinced by arguments of an outgroup (they do not trust these) More convinced by arguments of an ingroup (they trust these)

34 Mackie & Cooper (1984) persuaded by pro / anti arguments not persuaded

35 Talking Agent Listening Agent _________________________________________________________________ TopicArg1Arg2 Arg3 Arg4 Setting Agent  Listener trust to +1 for ingroup 0 for outgroup Prior Learning of Pro (Anti) Arguments #10 11 (-1) 1 (-1)1 (-1) 1 (-1) Talking #101i iii???? ? Test of Listener 1????

36 Mackie & Cooper (1984)

37 Referencing 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”

38 Referencing Paradigm

39

40 Talking Agent (“Director”) Listening Agent (“Matcher”) _________________________________________________________________ # of TrialsImage MartiniGlass Legs Each SideImage MartiniGlass Legs Each Side Setting “Director”  “Matcher” trust to +1 Setting “Director”  “Matcher” trust to 0 Prior Observation of Image by “Director” # Talking and Listening #4 first + 41i iii???? ? #2 ???? ? 1i iii Test of “Director”1???? of “Matcher” 1???? repeated expression condenses information: strong/weak features are polarized “Director” talks more so that features are stronger

41 Schober & Clark (1989)

42

43 Unique Information & Free Discussion Information sampling is biased, so that Shared information is communicated sooner and more often than Unshared information

44 Larson et al. (1996) more unshared information is communicated in the end

45 Talking Agent Listening Agent ______________________________________________________________________ # of TrialsPatient Shared1Shared2Unique1Unique2 Patient Shared1Shared2Unique1Unique2 Prior Learning # # Talking and Listening Shared1i i??? ??? 1i i Unique1ii? ?? ?? ? 1ii Test 1????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

46 Larson et al. (1996) more unshared information is communicated in the end

47 Gossip Paradigm Lyons & 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

48 Less spreading of SI elements More spreading of SI elements

49 Talking Agent Listening Agent ________________________________ ______________________________ Jamayan Smart StupidHonestLiar Prior SC (SI) Information on Jamayans: Each Agent #10 smart 11 #10 honest11 #10 stupid11 #10 liar11 Mixed (SC + SI) Story to Agent 1 #5 smart11 #5 liar11 Talking and Listening #5 intelligence1ii?? ? #5 honesty1i i?? ? Test: Each Agent smart1? stupid1? honest1? liar1?

50

51 Maxim of Quantity (Novelty) Talking agent Listening agent determines how much novelty in the information is expressed by the talker

52 Multi-Agent Model Agent 2 Listening Agent 1 Talking Jamayans Honest Smart Jamayans Honest Smart If receiving trust weight is high: If receiving trust weight is low: do the opposite boost activation (talk more on novel info) attenuate activation (talk less on known info) boost activation (talk more on novel info)

53 Gossip Paradigm: Lyons & Kashima (2003) Perceived Sharing of background information by 4 participants Knowledge (same information) >< Ignorance (different information)

54 Lyons & Kashima (2003) More spreading of SI elements Less spreading of SI elements

55 Talking Agent Listening Agent ________________________________ ______________________________ Jamayan Smart StupidHonestLiar 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 11 Mixed (SC + SI) Story to Agent 1 #5 11 Talking and Listening #5 1ii?? ? Test: Each Agent Smart1? Stupid1? Honest1? Liar1?

56 Lyons & Kashima (2003)

57 Gossip Paradigm: 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

58 Clark (2004) More spreading of SC elements (“grounding”)

59 Clark (2004)

60 Maxim of Quantity Brauer et al. (2001) Communication about Youth camp participants Actual Sharing of background information by 3 participants Dispersed (same SC + SI information) >< Concentrated (2 SC + 1 SI participant) Free discussion  Concentrated: less trust by and of SI participant more spreading of SI information

61 More spreading of SI elements Typical SC bias

62

63 Implications (take home lesson)

64 Implications 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

65 Implications 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

66 Implications 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

67 Unresolved Questions

68 Role of trust weights Talking agent Listening agent Maxim of quality (sensitivity): Presumably unconscious Maxim of quantity (novelty): Unconscious ?

69 Role of symbolic language Talking agent Listening agent Transformation of information in symbolic format (speech): does this influences its spreading?

70 Role 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)

71 Role of network structures Can the multi-agent network system develop efficient communication channels between agents ?

72 Role of leadership Leadership depends on trust social network central information exchange >< may lead to biased information spreading

73 Thank you


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