Answers to frequently asked questions about the online article Controlled Simulation of Marriage Systems published under NSF Grant BCS-9978282.› Published in the online Journal of Artificial Societies and Social Simulation, the article (see synoptic outline) deals with comparison of actual marriage behavior in a population with a simulation of random marriage behavior given the demographic constraints in the population.› The goal is to attain valid inferences about actual marriage preferences or rules in the population.› Applications to three case studies show that a radical unification of social science theories about norms and behavior in this field may be possible; one that overcomes the divisions between the behaviorist (norms as statistically frequent behaviors), the subjective idealist (norms as typical beliefs of individuals or inferred from answers on questionnaires) and the historical institutionalist approaches (the latter emphasizing the coherence of written law or case law, which may also act as a constraint on behavior, or žsocial factÓ impinging on individual behavior).
1. What are the audiences for this paper?
Sociologists will find the argument concerning global structures of large-scale cohesive social networks particularly relevant to the study of social class formation, elite studies, and the boundary conditions of large-scale social groupings (Sections 7, 8, 10, 11 cases 1 & 3, 12.1/3, 15, 16-18).
Anthropologists will find the identification of local structures (Sections 13) as well as certain global structures (such as dual organization, Section 14) of particular interest.
›››››››› Complexity theorists÷
2. Why are social rules seen as problematic?
Anthropologists, economists (e.g., in game theory or rational choice models), and sociologists pay great attention to social norms and rules, both in terms of what people say and what they do, which are often discrepant (as may be the case as well with legal norms vs. actual behavior).› Individual preferences and social žrules,Ó when put into practice, are subject to real-world constraints, unlike idealized subjective statements of norms.› This study questions the validity of a common habit of social science studies: to take the raw frequencies of behavior as the relevant evidence for inferring rules or preferences.› Looking at what people do, is it even possible, in principle, to find evidence of rules or preferences?› The answer being: yes, insofar as we take constraints into account, behavior that follows rules or preferences can be inferred if (1) it departs from what would be expected from žrandomÓ behavior under given similar constraints (in this case not only demographic but also legal, as when marriage prohibitions are imposed on the simulation model), and (2) there is empirical evidence supporting the mechanism by which preferential behaviors are implemented.›
3. What makes this a simulation?
The succession of generations is central to the simulation model: as relatives marry in early generations, they lay down patterns of relationships that will define the kinship links of succeeding generations.› This, along with demographic factors such as size and composition of sibling sets (given legal prohibitions on close blood marriages), affects the likelihood that marriages in subsequent generations will be with close kin, distant kin, or non-kin, or with particular types of blood kin (e.g., marriage of a man with MoBrDa).
4. What is the relevance of the findings?
Research findings to date in this field have led to two theoretical camps.› On one side, studies of social norms are often derided because of the lack of fit with actual behavior.› On the other, studies of actual behavior are derided as impossibly complex, in favor of subjective norms or rules (žideal modelsÓ).›
The research results from the case studies in the present article suggest radically different resolutions of this impasse.›
(1) Taking constraints into account to model the preferential or rule-governed component shows a much closer fit to normative models than hitherto recognized in kinship studies and theories of matrimonial alliance.
(2) The relevant žnormative rulesÓ or models, however, are rarely those identified by subjective normative theorists!
The bottom line: both camps are wrong, and a much higher level of social science modeling is possible.› In this approach, a greater degree of convergence is seen between certain expressed rules or norms or preferential behavior.› With this kind of triangulation of research results, we are able to come up with much better models of social or marriage systems than either the behaviorist or the idealist camps on their own.› One of the case studies also suggests a good fit with legal norms identified by the historical institutionalist approach, athough it will not in general be the case that written law is sociologically efficacious (but coherence in institutional case law would be expected to exert itself in constraints on individual behavior).
5. Does the approach generalize to other network problems?
Many aspects of this present approach, in a more general context of modeling network behavior under constraint, are already in use in network studies. Some recent examples in the domain of friendship networks include:
Zeggelink, Evelyn P.H. 1993. Strangers into Friends: the Evolution of Friendship Networks Using an Individual Oriented Modeling Approach. Amsterdam: ICS.
Moody, James. 1999. The Structure of Adolescent Relations: Modeling Friendship in Dynamic Social Settings. Ph.D. Dissertation: Department of Sociology, University of North Carolina.
The null hypothesis model of žrandom behavior under known constraintsÓ is important to network studies and essential to valid statistical inferences about the components of behavior.›
6. What are the implications and the next steps in this research?
The application of simulation models to the particular problem of population studies -- kinship and marriage networks in the context of history, ethnography, elite studies, studies of social class, etc. Ů is currently being extended to ask questions about žcomplex systemÓ dynamics and the phenomena of institutional emergence and change due to network processes and critical morphological or density thresholds.› These questions require a broader class of probabilistic simulation modeling involving social and demographic constraints, rules and agents in the general family of what we call network heterarchy.
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