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Belief Change via Social Influence and Explanatory Coherence. Bruce Edmonds Centre for Policy Modelling Manchester Metropolitan University. Context. - PowerPoint PPT Presentation
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Belief Change via Social Influence and Explanatory Coherence
Bruce EdmondsCentre for Policy Modelling
Manchester Metropolitan University
Context
• Dissatisfied with representing beliefs at “opinions” as a point on a continuous scale, since this confuses a measured effect with an underlying mechanism
• Wish to combine something of a cognitive model with social process of influence, since scientific belief is a combination of social and cognitive processes
• This is a “thought experiment” only, I hope that someone will point me to data to enable its assessment and development
Explanatory Coherence
• Thagard (1989)• A network in which beliefs are nodes, with
different relationships (the arcs) of consonance and dissonance between them
• Leading to a selection of a belief set with more internal coherency (according to the dissonance and consonance relations)
• Can be seen as an internal fitness function on the belief set (but its very possible that individuals have different functions)
• The idea of the presented model is to add a social contagion process to this
Adding Social Influence
• The idea is that a belief may be adopted by an actor from another with whom they are connected, if by doing so it increases the coherency of their set of beliefs
• Thus the adoption process depends on the current belief set of the receiving agent
• Belief revision here is done in a similar basis, beliefs are dropped depending on whether this increases internal coherence
• Opinions can be recovered in a number of ways, e.g. a weighted sum of belief presence or the change in coherence OR the change in coherence in the presence of a probe belief
Model Basics
• Fixed network of nodes and arcs• There are, n, different beliefs {A, B, ....}
circulating• Each node, i, has a (possibly empty) set of
these “beliefs” that it holds• There is a fixed “coherency” function from
possible sets of beliefs to [-1, 1]• Beliefs are randomly initialised at the start• Beliefs are copied along links or dropped by
nodes according to the change in coherency that these result in
Coherency Function
• Gives a measure of the extent to which different sets of beliefs are coherent
• Assumes a background of shared beliefs• Thus {A}0.5 and {B}{0.7} but {A, B}-0.4 if
beliefs A and B are mutually inconsistent• Different coherency functions will be applicable
to different sets of ‘foreground’ candidate beliefs and backgrounds of shared beliefs
• The probability of gaining a new belief from another or dropping an existing belief in this model is dependent on whether it increases or decreases the coherency of the belief set
Processes
Each iteration the following occurs:• Copying: each arc is selected; a belief at the
source randomly selected; then copied to destination with a probability linearly proportional to the change in coherency it would cause
• Dropping: each node is selected; a random belief is selected and then dropped with a probability linearly proportional to the change in coherency it would cause
• -11 change has probability of 1• 1-1 change has probability of 0
Illustration
Opinion dynamics models, Nania, Edinburgh, August 2007, http://cfpm.org/nania slide-8
A
B
C
AB
Copying
C
C
Dropping
A
Example of the use of the coherency function• coherency({}) = -0.65• coherency({A}) = -0.81• coherency({A, B}) = -0.37• coherency({A, B, C}) = -0.54• coherency({A, C}) = 0.75• coherency({B}) = 0.19• coherency({B, C}) = 0.87• coherency({C}) = -0.56• A copy of a “C” making {A, B} change to {A, B, C} would
cause a change in coherence of (-0.37--0.54 = 0.17)• Dropping the “A” from {A, C} causes a change of -1.31
Consensus with different connectivity (bi-directional arcs)
0
5
10
5 200
5
10
15
20
25
Consensus of different coherence functions (20 uni-directional arcs)
Nania Final Meeting, Edinburgh, August 2008, http://cfpm.org/nania slide-11
0
5
10
5 200
decreasingFunction
doubleFunction
increasingFunction
randomConsistency
singleFunction
zeroConsistency
20 arcs - 10 nodes - 3 tags - cr .5 dr .5 - init prob 0.5 - diff uni-nets - selection con fns- PD
Consensus of different coherence functions (10 bi-directional arcs)
Nania Final Meeting, Edinburgh, August 2008, http://cfpm.org/nania slide-12
0
5
10
5 200
decreasingFunction
doubleFunction
increasingFunction
randomConsistency
singleFunction
zeroConsistency
10 arcs - 10 nodes - 3 tags - cr .5 dr .5 - init prob 0.5 - diff Bi-nets - selection con fns- PD
Consensus of different coherence functions (20 uni-directional arcs, only drop incoherent)
Nania Final Meeting, Edinburgh, August 2008, http://cfpm.org/nania slide-13
0
5
10
5 200
decreasingFunction
doubleFunction
increasingFunction
randomConsistency
singleFunction
zeroConsistency
20 arcs - 10 nodes - 3 tags - cr .5 dr .5 - init prob 0.5 - diff uni-nets - selection con fns- ODI
Example – fixed random coherency function – Fixed Random Fn
A B C
ABC
AB BCAC
-0.65
-0.81 0.19 -0.56
-0.54
-0.37 0.870.75
“Density” of A for different sized networks – Fixed Random Fn
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
5 80 155 230 305 380 455
5
10
15
20
25
“Density” of C for different sized networks – Fixed Random Fn
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
5 80 155 230 305 380 455
5
10
15
20
25
Number of Beliefs Disappeared over time, different sized networks – Fixed Random Fn
0
0.5
1
1.5
2
2.5
3
5 10 15 20 25 30 35 40 45 50Nu
mb
er
of B
elie
fs D
isa
ppe
are
d b
y tim
e 5
00
Network Size
Av. Resultant Opinion – Fixed Random Fn
Consensus – Fixed Random Function
Zero Function
A B C
ABC
AB BCAC
0
0 0 0
0
0 00
Consensus – Zero Fn
Single Function
A B C
ABC
AB BCAC
0
1 1 1
-1
-0.5 -0.5-0.5
Consensus – Single Fn
Av. Resultant Opinion – Single Fn
Prevalence of Belief Sets Example – Single
Double Function
A B C
ABC
AB BCAC
-1
0 0 0
-1
1 11
Consensus – Double Fn
Prevalence of Belief Sets Example – Double Fn
Av. Av. Resultant Opinion
Av. Consensus, Each Function
Effect of Number of Beliefs and Hardness of Coherency Function
Effect of Number of Agents, Drop Rat and Coherency Hardness
Effect of Network Structure with Contrasting Coherency Functions
Comparing with Evidence
• Lack of available cross-sectional AND longitudinal opinion studies in groups
• But it can be compared with broad hypotheses– Consensus only appears in small groups (balance of
beliefs in bigger ones)– Big steps towards agreement appears due to the
disappearance of beliefs– (Mostly) network structure does not matter– Relative coherency of beliefs matters– Different outcomes can result depending on what gets
dropped (in small groups)• How the model responds to different agents with
different consistency functions not yet examined
Future Work
• Validation! Finding suitable data sets where the coherency function can be estimated and time series of outcomes can be obtained
Possible extensions of model:• Making the model less noisy with a threshold for
coherency change (a minimum change of coherency for a change to occur)
• Agents with different coherency functions interacting in the same network
• Changing social network, maybe with belief homophily so that one is more likely to influence those with more similar beliefs
The End
Bruce Edmonds
http://bruce.edmonds.name
Centre for Policy Modelling
http://cfpm.org
A version of these slides is at: http://slideshare.com/BruceEdmonds
The simulation is available at: http://cfpm.org/models
“ACS model -v2.2.nlogo”