Computer Modeling of Small Group Interactions

1. Computer Modeling of Small Group Interactions David Heise September 30, 2011 Social Psychology, Health, and Life Course Workshop Indiana University, Bloomington

2. Preview of this talk Theory Development Computer Model of Groups Results Using the Program

3. A definition:

4. 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.

5. 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.

6. 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

7. A computer model of Juries

8. 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.

9. 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.

10. 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.

11. 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.

12. 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.

13. The process of agent interaction • 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.

14. Circle diagram Actioncomplete How theprogramworks

15. 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

16. Results when actor, object, and behavior are selected randomly

17. Random model:Distributions of acts in IPA categories Random Observed Juries Correlation: r = .09

18. 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.

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

20. Results from theory-based model—high-tension actor selects object and behavior to minimize tension

21. Theory-based model. Distributions of acts in IPA categories Theory-based model Observed Juries Based on 17of 29 juries Correlation– ABM versus Empirical: r = .90

22. Gender differences in IPA Theory-based Model Empirical observations Based on 12 of 29 juries Correlation of sex differences – ABM versus Empirical: r = .86

23. Gender differences in initiation of acts Theory-based Model Empirical observations

24. % of acts by ranks 1, 2-3, 4-6, 7-12 Theory-based Model Empirical observations

25. 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, • Group process develops as members act to confirm their in-group identities. • The individual whose personal identity is most stressed is the one who acts next. • 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.

26. Applying GroupSimulator

27. GroupSimulator looks like this at the beginning of an analysis.

28. GroupSimulator looks like this after some actions have occurred

29. Group Dynamics

30. 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.

31. Yourturn! You can find more on ACT at www.indiana.edu/~socpsy/ACT/ Click the Interact button to get to GroupSimulator and a guide to GroupSimulator.

32. Abstract An 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.

33. Bales’ Interaction Process Analysis(IPA ) Expressive-integrative positive reactions 1 Shows solidarity (help, compliment, gratify) 2 Shows tension release (josh, laugh with, cheer) 3 Agrees (agree with, understand, accommodate) Instrumental-adaptive attempted answers 4 Gives suggestion (encourage, cue, coach) 5 Gives opinion (persuade, influence, interest) 6 Gives orientation (inform, educate, explain to) Instrumental-adaptive questions 7 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 reactions 10 Disagrees (disagree with, ignore, hinder) 11 Shows tension (fear, cajole, evade) 12 Shows antagonism (argue with, deride, defy)

34. Relevant Publications Bales, 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.

35. 1950s Action Researchers 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 Strodtbeck Harvard Chicago Bales Harvard Shils

36. Values in Bales’ SYMLOG System

37. 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.