eth d-gess: 851-0585-37l · pdf fileintangible “force” over a group. ... finally,...

||Department of Humanities, Social and Political Sciences

Program in Computational Social Science

Social Modelling, Agent-Based

Simulation and Collective Intelligence(Week 9)

11.04.2016 1

ETH D-GESS: 851-0585-37L

Ovi Chris Rouly, PhD



ETH D-GESS: 851-0585-37L Week 9

11.04.2016Ovi Chris Rouly, PhD 2

Social Norms

and

Opinion Formation


Program in Computational Social Science11.04.2016Ovi Chris Rouly, PhD 3

This lesson considers formal models of the social phenomena norms

and opinion formation. In general, a (social) norm acts like an

intangible “force” over a group. It drives the individuals in the group

toward conformity. Externally, it resembles an adjustable set-point.

Norms emerge endogenously (as systemic processes), exogenously

(e.g. due to circumscription), or both. Opinion formation, on the other

hand, is a process through which an individual (or a group) undergoes

convergence toward (or away-from) an attitude-biased position

moderated by subjective, individual, and often semi-permanent values.

We can quantify these phenomena only through their proxy behavior(s).

The two phenomena are closely related. It is often difficult to judge if

they emerge from within the individuals (going into the group to be fed-

back to the individuals), or if the information flows the other way around.

Let’s get started!



Social Modelling, Agent-Based

Simulation and Collective Intelligence

Course Overview

Procedure (Parts I & II):

1. Examine a selection of published, formal models of social processes

2. Learn how to analyze and extend simple models and to develop your own

social process models using existing computer-coded examples



Are the processes associated with creating social norms strictly cultural?


Social Norms



Graphic taken from https://commons.wikimedia.org/w/index.php?curid=36619962

S. Asch (1951)

Reenactment of Soloman Asch’s Experiment

https://www.youtube.com/watch?v=sno1TpCLj6A



Solomon Asch Group Conformity Experiment(Effects of group pressure upon the modification and distortion of judgments - S. Asch,1951)

18 Trials with 50 experimental- & 37 control-subjects

- Control Results: assessments ~99% accurate

- Experimental Results: assessments ~63% accurate

~37% inaccurate

- Of the inaccurate assessments (the experimental group’s ~37%):

- ~5% simply went with group and were inaccurate

- ~25% defied group (but were still inaccurate)

- ~70% sometimes went with group sometimes not still inaccurate

- Findings:

- of the ~70% “sometimes” group followers there were 3 error types- Type 1 “distortion of perception” poor vision, hearing, thinking, etc.

- Type 2 “distortion of judgment” failure to believe in self

- Type 3 “distortion of action” failure to act when accuracy was possible

Replication success: Deutsch & Gerard, 1955; Larson, 1990; ----- Replication failure: Perrin and Spencer, 1980



Are the processes associated with creating social norms strictly cultural?

Do the mechanisms of social conformity (“norms”) have an evolutionary basis?


Social Norms



From “Towards Emergent Social Complexity”, Rouly, 2016, p. 7.




An evolutionary approach to Norms (Axelrod, 1986)

Are the mechanisms employed by social norm convergence evolutionary?



Axelrod, 1986; Janssen & Jager, 1999; Deffuant, Amblard, Weisbuch, & Faure, 2002; Hegselmann & Krause, 2002;

Deffuant, 2006; Mckeown & Sheehy, 2006; and many others, for example Stauffer, Sousa, & Schulze, 2004, etc.


Research on norms and

opinion formation suggest they

can be explained by game

theoretic processes producing

emergent outcomes across a

agent-oriented network topology.



Claims of evidence of sufficiency over a constrained network may require caution.


A game-theoretic model can be powerful and it can offer an explanation of

behavior in terms of ultimate causality (Tinbergen, 1951). However, we

may want to ask if its conclusions are constrained to a particular network.

Consider what Hegselmann and Krause (2002) said in a similar regard: "...

simulations show that ... locality matters dramatically ..." And that, in certain

types of, "... [network] neighbourhoods in which the agents interact ..." then,

"the phase in between plurality and [norm] consensus, i.e. polarization,

disappears" (p. 29).

Game-theoretic models provide a necessary piece of the proof for

many social questions but are they always sufficient?



We are looking for examples of formal behavioral

models that also contains an explanation of the

proximate causes of the behaviors of opinion

formation and for norm emergence more generally.

Remember, the agent-based simulation paradigm is

concerned with the behavior of the individuals; not just

the overall system. So, have we exhausted the

explanatory power of the ABM paradigm to find

alternative models for this aspect of sociality?

In the ethology of Tinbergen (1951), distinguishing between proximal and

ultimate behavioral causality was key to beginning the process of understanding

how and why a species had this or that particular isopraxis (MacLean, 1975).




No. Not yet.



“An integrated approach to simulating behavioural processes: A case study of the

lock-in of consumption patterns” (Janssen & Jager, 1999)


Driving the Simulation of Opinion Formation

(“Lock-in”) as a Cognitive Process

individual decision making agents

with cognitive behavior grounded in

empirical data and construct theory



Steering agent behavior by a pseudo-cognitive process. Janssen & Jager (1999)


Opinion Formation (“Lock-in”) Steered by a Cognitive Model

Uncertainty (Unc)

Level of Need Satisfaction (LNS)

Cellular Automata Basis



After a break will we continue our discussion of formal models of

norms and opinion formation. We will ask if psychological

components (like those in Janssen and Jager, 1999) improve the

validity of our complex social systems models. Would adding

explicit biological, ecological, and or adaptive components improve

them even further? Finally, we will turn to a NetLogo-coded

example of opinion formation for a simple run-time example.

This week there are no new writing assignments. There are

however, two reading assignments whose subject matter will make

an appearance on the final exam.



5-6 minutes


break



Albert Einstein


"Things should be made as simple as possible - but no simpler."



1. Social norms are emergent products from a normative process

• the normative (social) process appears evolutionary (Axelrod, 1986)

• individual norm compliance is volitional but has consequences

• norm reinforcement involves (social group) feedback and possibly

circumscription (Carneiro, 1987)

2. Opinion formation is a process that produces a set of opinions

• an opinion is a subjective cognition produced by the “self”

• agents build opinions through cognition, emotion and “other” contact

• a set of opinions is formed as an emergent result over the group.

Social Norms and Opinion Formation



“Unfortunately, comparatively little

attention has been devoted to the problem

of cooperation between groups with

different preferences (e.g. people of

different gender, status, age, or cultural

background)” Helbing, 2012, p. 185).



The Models



Every epoch “compliant” individuals are replicated and those less so removed.


Concepts:

CSS modeling paradigm – ABM

Simple tools – abstract game theoretic approach

Research hypothesis – “norms” emerge due to social penalties for non-compliance

An evolutionary approach to Norms (Axelrod, 1986)

Agent properties: { quantities –

boldness and vengefulness }

Rules:

All agent properties are initialized

randomly. Each epoch they are

paired with another agent and get to

choose to cooperate or defect based

on a biased (bold/vengeful)

predisposition. Results are assigned.



Lattice type plays a role in diffusion between extremum but noise does not.


Concepts:

CSS modeling paradigm – network interconnected lattice topology

Mechanism hypothesis – influence between correspondents is transfer

mechanism dependent both according to lattice and distribution of similarity

Comparing Extremism Propagation Patterns in Continuous

Opinion Models (Deffuant, 2006)

Agent properties: { opinion x }

Rules:

A set of agents are initialized on

one of four network types.

During runtime “opinions,” as

real-values, are introduced and

allowed to diffuse across the

network. Additionally, noise is

introduced to determine its

effects on “opinion” propagation.



Graded viewpoints (opinions) will disperse more effectively between limits.


Mass Media and Polarization Processes in the Bounded

Confidence Model of Opinion Dynamics (Mckeown & Sheehy, 2006)


Rules:

A set of agents are initialized on a network.

During runtime “opinions,” as real-values,

are introduced exogenously as “media”

announcements. These diffuse across the

network and the results are observed.

Concepts:

CSS modeling paradigm – agents on a

network interconnected lattice topology

Mechanism hypothesis – influence between correspondents is transfer

mechanism dependent but can be changed by outside media inputs.

Opinion

adjustment:



Highly interconnected agents can better resolve informational contradictions.


BRADFORD SOCIAL NORMS 1-31-2014 Emperors Dilemma

NetLogo Instantiation (John Hamilton Bradford 01/31/2014)


Rules:

Based on Centola, D., Willer, R., & Macy,

M. (2005). The Emperor’s Dilemma: A

Computational Model of Self‐Enforcing

Norms. American Journal of Sociology,

110(4), pp. 1009-1040.

Concepts:

CSS modeling paradigm – agents on a

network interconnected lattice topology

Mechanism hypothesis – more fully connected agents will resolve the dilemma

of a false norm better than more broadly unconnected agents.



Week 9 deliverables: Reading and accountability


Reading assignments:

Grimm, V., Berger, U., DeAngelis, D. L., Polhill, J. G., Giske, J., & Railsback,

S. F. (2010). The ODD protocol: a review and first update. Ecological

modelling, 221(23), 2760-2768.

Axtell, R. L., Epstein, J. M., Dean, J. S., Gumerman, G. J., Swedlund, A. C.,

Harburger, J., ... & Parker, M. (2002). Population growth and collapse in a

multiagent model of the Kayenta Anasazi in Long House Valley. Proceedings

of the National Academy of Sciences, 99(suppl 3), 7275-7279.

Writing/Coding assignment:

None.

Deliverables this week



• Asch, S. E. (1951). Effects of group pressure upon the modification and distortion of judgments.

Groups, leadership, and men. S, pp. 222-236.

• Axelrod, R. (1986). An evolutionary approach to norms. American political science review, 80(04), pp.

1095-1111.

• Carneiro, R. (1987). Further reflections on resource concentration and its role in the rise of the state.

in Manzanilla, L. (ed.) Studies in the Neolithic and Urban Revolutions. Oxford: BAR International

Series 349. pp. 245-260.

• Centola, D., Willer, R., & Macy, M. (2005). The Emperor’s Dilemma: A Computational Model of

Self‐Enforcing Norms1. American Journal of Sociology, 110(4), pp. 1009-1040.

• Concept after Chapais, B. (2008). Primeval kinship. Cambridge, Mass.: Harvard University Press.

• Chapais, B. (2008). Primeval kinship. Cambridge, Mass.: Harvard University Press.

• Chekroun, P., & Brauer, M. (2002). The bystander effect and social control behavior: The effect of the

presence of others on people's reactions to norm violations. European Journal of Social Psychology,

32(6), pp. 853-867.

• Deffuant, G. (2006). Comparing extremism propagation patterns in continuous opinion models.

Journal of Artificial Societies and Social Simulation, 9(3).

• Deutsch, M., & Gerard, H. B. (1955). A study of normative and informational social influences upon

individual judgment. The journal of abnormal and social psychology, 51(3), p. 629.

• Hegselmann, R., & Krause, U. (2002). Opinion dynamics and bounded confidence models, analysis,

and simulation. Journal of Artificial Societies and Social Simulation, 5(3).

• Helbing, D. (Ed.). (2012). Social self-organization: Agent-based simulations and experiments to study

emergent social behavior. Springer.

REFERENCES



• http://ccl.northwestern.edu/netlogo/community/BRADFORD%20SOCIAL%20NORMS%201-31-

2014%20Emperors%20Dilemma.ninfo accessed on 4 April 2016, 16:17.

• Larsen, K. S. (1990). The Asch conformity experiment: Replication and transhistorical comparisons.

Journal of Social Behavior & Personality.

• MacLean, P. D. (1975). On the evolution of three mentalities. Man-Environment- Systems 5, 213-224.

Reprinted 1977, in: New Dimensions in Psychiatry: A World View, Volume 2 (S. Arieti and G.

Chrzanowski, eds.), Wiley, New York, pp. 305-382. Reprinted 1978, in: Human Evolution, Biosocial

Perspectives (S. L. Washburn and E. R. McCown, eds.), Benjamin/Cummings, Menlo Park, Calif. pp.

32-57.

• Mahmoud, S., Griffiths, N., Keppens, J., & Luck, M. (2010). An analysis of norm emergence in

Axelrod's model.

• Mckeown, G., & Sheehy, N. (2006). Mass media and polarisation processes in the bounded

confidence model of opinion dynamics. Journal of Artificial Societies and Social Simulation, 9(1).

• Perrin, S., & Spencer, C. (1980). The Asch effect: a child of its time. Bulletin of the British

Psychological Society. 32(405). p. 6.

• Tinbergen, N. (1951). The study of instinct. Oxford: Clarendon Press.

REFERENCES

http://ccl.northwestern.edu/netlogo/community/BRADFORD SOCIAL NORMS 1-31-2014 Emperors Dilemma.ninfo



we will see models of crowd disasters and pedestrian traffic

models of abstract social systems and a historical culture

consider explicit models and their potential utility

and, decide if we think Collective Intelligence can be instantiated

In the weeks that follow we will:



ETH Zurich

D-GESS Computational Social Science

Clausiusstrasse 50

8006 Zürich, Switzerland

http://www.coss.ethz.ch/

Ovi Chris Rouly, PhD.

Email: [email protected]

Telephone: (41) 044-633-8380

© ETH Zurich, 11 April 2016


Contact information

http://www.coss.ethz.ch/

mailto:[email protected]



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