Computer Modeling of Small Group Interactions

David HeiseSeptember 30, 2011

Social Psychology, Health, and Life Course Workshop Indiana University, Bloomington

Preview of this talk

Computer Model of Groups

Results

Theory Development

Using the Program

A definition:

Theorizing about social interaction

• Start with affect control theory. With actor and object given, ACT specifies the behavior that best confirms the affective meanings of the actor, behavior, and object. – These specifications predict role behavior, and have

been validated empirically—see Expressive Order.• An extension of ACT: a given actor will choose as

behavioral object the group member with whom the actor can build the most confirming action. – Experimentally validated by Wiggins and Heise.

Choosing the actor for the next turn

• Conversation analysts have identified various structures in which two individuals alternate actor-object positions (e.g., question-answer)– But CA offers no guidance on who in a group gets the

next turn outside of such structures• Hypothesis: The next actor will be the group

member for whom self-impression most deviates from self-sentiment—the one with most self-tension.– Action is a resource for reducing tension, and the

individual in a group who most needs this resource seizes the moment, while others yield the floor.

Measuring Affect• Sentiments and impressions are measured on three

dimensions ranging from -4 to +4.

• The dimensions are cross-cultural universals validated in dozens of cultures. See Surveying Cultures for details.

• Some examples (2004 measurements):– Defense attorney: 0.90 1.74 1.36 – Juror: 0.81 1.88 -0.18 – Defendant: -0.13 -0.57 -0.25

A COMPUTER MODEL OF JURIES

The data to be modeled

• Data collected on juries by Chicago sociologist, Fred Strodtbeck, are vintage (1950s), but unique.

• 29 mock trials with jurors drawn by lot in sets of 12 from the regular jury pool of Chicago.

• Jurors listened to a recorded trial, deliberated, and returned their verdict, under the supervision of a court baliff.

• Deliberations were observed and actions scored into the twelve categories of Bales’ Interaction Process Analysis.

• Counts of acts-initiated were tallied for males and females, and by rank-order of initiation levels.

Relating theorizing to observations of juries—

AGENT-BASED MODELING• An agent-based model consists of computer-

driven agents who act and react on each other within the constraints of theoretical principles, in order to clarify social processes.

• A program called GroupSimulator implements an agent-based model of juries, has juries interact for a while, and measures the results.

Essentials of GroupSimulator’smodel of juries

• 12 jurors, each operating as the theory specifies. • 1/3 females as in 1950s juries.• Conversational structures incorporated by forcing

some sequences of actor-object alternation.• 40% of actions are directed toward the whole

group, as in real groups.– A whole-group action distributes to each member—

e.g., if a group member praises the group, all the other members feel personally praised.

The agents• Males take the identity of juror, females take the

identity of female juror.In mid-20th Century: For a woman in a field usually reserved for males, “her anomalous position must be marked by the addition of a special, female-specific marker … lady doctor, a female surgeon, women lawyers, and lady sculptors.... We understand any noun that occurs in its 'unmarked' form to refer to a male.”

• The 1970s EPA sentiment associated with juror was – 0.8 1.6 -0.5—slightly good, quite powerful, and

somewhat quiet. • The profile for female juror was

– 1.2 0.7 0.0—nicer, less powerful, and not so quiet.

The agents (cont.)

• Agents personal identities in the situation vary around their basic identity of juror or female juror, creating individualities like arrogant juror, earnest juror, meek juror, and trusting juror.– GroupSimulator generates these variations randomly,

different in each simulated jury.• Impression-formation equations allow agents to

process events and to work out sentiment-confirming behaviors.– Impression formation is described in Expressive Order.

• Confirming self-sentiments and the self-sentiments of others motivates agents to action.

• The agent with greatest self-tension is the next actor.– Except sometimes the last object of action becomes the next

actor, yielding sequences of actor-object alternation that mimic conversational structures like question-answer.

• The actor chooses as object the group member who permits the most sentiment-confirming action.– Except sometimes the actor addresses the whole group whether

or not this is the most sentiment-confirming action.• The actor performs the behavior that best moves

impressions of the actor and object close to self-sentiments.– The action changes impressions of actor and object, thereby

affecting subsequent events.

The process of agent interaction

Circle diagramFind object of action

Derive confirming behavior

Compute impressions

and emotions

Find next actor

Actioncomplete

How theprogram

works

The Analyses

• Each simulated jury was run through 1,000 actions, which is about equivalent to one hour of deliberation

• Statistics are based on 500 simulated juries … 500,000 actions.• Ideally, simulation statistics will match the contours of

statistics from empirical observations.

• In a first set of analyses I made GroupSimulator select everything randomly—actor, object, and behavior—in order to provide a basis for comparison

RESULTS WHEN ACTOR, OBJECT, AND BEHAVIOR ARE SELECTED RANDOMLY

Random model:Distributions of acts in IPA categories

Random Observed Juries

Correlation: r = .09

Random model and gender differences

• Observed juries had substantial gender differences in IPA distributions: males higher in giving orientation; females higher in expressive-integrative actions. – Random model showed no gender differences in IPA

distributions because behaviors were selected randomly for both males and females.

• In observed juries, males initiated 79% of acts, females 21%, though membership percents were 68% and 32% ― males were more talkative. – In random model males initiated 67%, females 33%, the

same as membership percents.

Rank-frequency relation. % of acts by ranks 1, 2-3, 4-6, 7-12

Random Observed Juries

RESULTS FROM THEORY-BASED MODEL—HIGH-TENSION ACTOR SELECTS OBJECT AND BEHAVIOR TO MINIMIZE TENSION

Theory-based model. Distributions of acts in IPA categories

Theory-based model Observed Juries

Correlation– ABM versus Empirical: r = .90 Based on 17of 29 juries

Gender differences in IPA

Theory-based Model Empirical observations

Correlation of sex differences – ABM versus Empirical: r = .86 Based on 12 of 29 juries

Gender differences in initiation of acts

Theory-based Model Empirical observations

% of acts by ranks 1, 2-3, 4-6, 7-12

Theory-based Model Empirical observations

Some observations• The theory-based model matched the contours of empirical

data much better than the random model.• This theory-based model was better than some other theory-

based models (not shown).• Model validation lent support to key assumptions in the model

—notably, 1. Group process develops as members act to confirm their in-group

identities.2. The individual whose personal identity is most stressed is the one

who acts next.3. Females’ gender-marked identities in the 20th Century explained

differences in female participation, as opposed to the structural-functional explanation that the human sexes have different societal functions.

APPLYING GROUPSIMULATOR

GroupSimulator looks like this at the beginning of an analysis.

GroupSimulator looks like this after some actions have occurred

Group Dynamics

Possible analyses with GroupSimulator

• Explore groups of different kinds: work groups and military groups, and also sports fans, vacationers, street people, gangsters.

• Discover bases of network emergence.• Explore various bases for turn-taking in groups,

such as values, or homophily.• Examine the emotional consequences of

conversational structures that alternate actor and object, or of actions toward whole group.

Yourturn!

You can find more on ACT atwww.indiana.edu/~socpsy/ACT/

Click the Interact button to get to GroupSimulator and a guide to GroupSimulator.

AbstractAn agent-based model of juries is presented in which agents interact in order to maintain their self-sentiments, comprised of the affective meaning of the juror identity combined with personal traits like industrious, self-righteous, or tolerant. The model successfully reproduces features observed in 20th Century jury deliberations: 1. actions mainly are in categories of giving opinions and providing orientation; 2. females specialize in expressive-integrative actions; 3. males initiate proportionately more actions than females; and 4. the most-active agents initiate many times more actions than average agents. The simulation program is available on the web and can be used for studying various kinds of groups.

Bales’ Interaction Process Analysis(IPA )

Expressive-integrative positive reactions1 Shows solidarity (help, compliment,

gratify)2 Shows tension release (josh, laugh with,

cheer)3 Agrees (agree with, understand,

accommodate)Instrumental-adaptive attempted

answers4 Gives suggestion (encourage, cue,

coach)5 Gives opinion (persuade, influence,

interest)6 Gives orientation (inform, educate,

explain to)

Instrumental-adaptive questions7 Asks for orientation (quiz, question,

ask about something)8 Asks for opinion (consult, prompt,

query)9 Asks for suggestion (entreat, ask,

beseech)Expressive-integrative negative

reactions10 Disagrees (disagree with, ignore,

hinder)11 Shows tension (fear, cajole, evade)12 Shows antagonism (argue with,

deride, defy)

Relevant PublicationsBales, Robert Freed. 1951. Interaction Process Analysis: A Method for the Study of Small Groups.

Cambridge MA: Addison-Wesley._____. 1953. "The equilibrium problem in small groups." Working Papers in the Theory of Action. T.

Parsons, R. F. Bales and E. A. Shils. Glencoe IL, Free Press: 424-456._____. 1999. Social Interaction Systems: Theory and Measurement. New York: Transaction.Bosmajian, H. 1977. "Sexism in the language of legislatures and courts." Pp. 77-104 in Sexism and

Language, edited by A. P. Nilsen, H. Bosmajian, H. L. Gershuny, and J. P. Stanley. Urbana IL: National Council of Teachers of English.

Gilbert, Nigel. 2008. Agent-Based Models. Thousand Oaks CA: Sage.Heise, David R. 2007. Expressive Order: Confirming Sentiments in Social Actions. New York: Springer._____. 2010. Surveying Cultures: Discovering Shared Conceptions and Sentiments. Hoboken, NJ: Wiley

Interscience.MacKinnon, Neil J., and David R. Heise. 2010. Self, Identity, and Social Institutions. New York: Palgrave.Schegloff, E. A. 2007. Sequence Organization in Interaction: A Primer in Conversation Analysis. New York:

Cambridge University Press.Stanley, J. P. 1977. "Gender-marking in American English: Usage and reference." Pp. 43-74 in Sexism and

Language, edited by A. P. Nilsen, H. Bosmajian, H. L. Gershuny, and J. P. Stanley. Urbana IL: National Council of Teachers of English.

Strodtbeck, Fred L., and R. D. Mann. 1956. "Sex Role Differentiation in Jury Deliberations." Sociometry 19(1):3-11.

Wiggins, Beverly B. and David R. Heise. 1988. "Expectations, intentions, and behavior: Some tests of affect control theory." Pp. 153-170 in Lynn Smith-Lovin and D. Heise (Eds.), Analyzing Social Interaction: Advances in Affect Control Theory. New York: Gordon and Breach.

Wilensky, Uri, and W. Rand. in press. An Introduction To Agent-Based Modeling: Modeling Natural, Social and Engineered Complex Systems With NetLogo. Cambridge, MA: MIT Press.

Harvard

Robert Freed Bales. 1951. Interaction Process Analysis: A Method for the Study of Small Groups. Cambridge MA, Addison-Wesley.Talcott R. Parsons and Edward A. Shils, Eds. (1951). Toward a General Theory of Action. Cambridge, MA, Harvard University Press.T. R. Parsons, R. F. Bales, and E. A. Shils. 1953. Working Papers in the Theory of Action. Glencoe IL:, Free Press.T. R. Parsons and R. F. Bales (1955). Family, Socialization and Interaction Process. Glencoe IL, Free Press.

Parsons

Bales

Chicago

Shils

Strodtbeck

Harvard

1950s Action Researchers

Values in Bales’ SYMLOG System

Low Activity Medium Activity High Activity

Potency Positive EvaluationHigh PF: love, tenderness,

sympathy, affectionUPF: progress, authority, unanimity, orthodoxy

UP: popularity, cooperation, friendliness

Medium DPF: equality, conformity, love, religion

P: democracy, equality, friendliness; autonomy

UPB: relativism, permissive, equality, nurturance

Low DP: equality, trust in human goodness

DPB:, underdogs (like the poor), anti-establishmentarianism

PB: trust, appreciating others

Medium EvaluationHigh F: supernatural, immortality,

religion, anti- scienceUF: cooperation, loyalty, harmony

U: money, power, success, competition

Medium DF: contemplation, reverie, moderation

Ambivalence, uncertainty UB: relativism, situational ethics, acting out

Low D: mind control, asceticism, mysticism

DB: martyrdom, superficial expressiveness, gratification

B: expressiveness, gratification, anti-religion, anti-establishment

Negative EvaluationHigh NF: conscience, moderation,

restraintUNF: inhibition, discipline, purity UN: determination, struggle,

adventure, dangerMedium DNF: suffering, sacrifice, the

everlasting, moralityN: independence, privacy, detachment, rationality

UNB: sensuality, action, change, nonconformity, self-reliance

Low DN: homeliness, aloofness, anti-materialism

DNB: simplicity, roughness, self-sufficiency

NB: conservatism, non-cooperative, cynicism, isolation

GroupSimulator Details

• Developed in NetLogo, a programming environment for agent-based models—see forthcoming Wilensky and Rand book, or http://ccl.northwestern.edu/netlogo/

• About 2,500 lines of NetLogo code, 43 procedures.• Quantitative analyses manipulate 9x75 matricies.

Several 27-element lists provide verbal output.• Four breeds of agents, two of which perform actions. • Each active agent owns 22 variables, including lists of

numbers.