Case-based methods and agent-based modelling: bridging the divide to leverage their combined strengths

Two leading camps for studying social complexity are case-based methods (CBM) and agent-based modelling (ABM). Despite the potential epistemological links between ‘cases’ and ‘agents,’ neither camp has leveraged their combined strengths. A bridge can be built, however, by drawing on Abbott’s insight that ‘agents are cases doing things’, Byrne’s suggestion that ‘cases are complex systems with agency’, and by viewing CBM and ABM within the broader trend towards computational modelling of cases. To demonstrate the utility of this bridge, we describe how CBM can utilise ABM to identify case-based trends; explore the interactions and collective behaviour of cases; and study different scenarios. We also describe how ABM can utilise CBM to identify agent types; construct agent behaviour rules; and link these to outcomes to calibrate and validate model results. To further demonstrate the bridge, we review a public health study that made initial steps in combining CBM and ABM.     

Analyzing event stream dynamics in two-mode networks: An exploratory analysis of private communication in a question and answer community

Information about social networks can often be collected as event stream data. However, most methods in social network analysis are defined for static network snapshots or for panel data. We propose an actor oriented Markov process framework to analyze the structural dynamics in event streams. Estimated parameters are similar to what is known from exponential random graph models or stochastic actor oriented models as implemented in SIENA. We apply the methodology on a question and answer web community and show how the relevance of different kinds of one- and two-mode network structures can be tested using a new software.     

Dynamic Discrete-time Duration Models: Estimation via Markov Chain Monte Carlo

Discrete-time or grouped duration data, with one or multiple types of terminating events, are often observed in social sciences or economics. In this paper we suggest and discuss dynamic models for flexible Bayesian nonparametric analysis of such data. These models allow simultaneous incorporation and estimation of baseline hazards and time-varying covariate effects, without imposing particular parametric forms. Methods for exploring the possibility of time-varying effects, as for example the impact of nationality or unemployment insurance benefits on the probability of reemployment, have recently gained increasing interest. Our modeling and estimation approach is fully Bayesian and makes use of Markov Chain Monte Carlo (MCMC) simulation techniques. A detailed analysis of unemployment duration data, with full-time job, part-time job and other causes as terminating events, illustrates our methods and shows how they can be used to obtain refined results and interpretations.     

Placing Families in Context: Challenges for Cross‐National Family Research

Cross‐national comparisons constitute a valuable strategy to assess how broader cultural, political, and institutional contexts shape family outcomes. One typical approach of cross‐national family research is to use comparable data from a limited number of countries, fit similar regression models for each country, and compare results across country‐specific models. Researchers increasingly are adopting a second approach, which requires merging data from many more societies and testing multilevel models using the pooled sample. Although the second approach has the advantage of allowing direct estimates of the effects of nation‐level characteristics, it is more likely to suffer from the problems of omitted‐variable bias, influential cases, and measurement and construct nonequivalence. The author discusses ways to improve the first approach's ability to infer macro‐level influences as well as how to deal with challenges associated with the second one. She also suggests choosing analytical strategies according to whether the data meet multilevel models' assumptions.     

Some Log-Linear and Log-Nonlinear Models for Ordinal Scales With Midpoints, With An Application to Public Opinion Data

Ordinal response scales with a middle category are widely used in public opinion studies, psychology, medicine, computed tomography and other fields. The usual models in the statistical literature for ordinal response variables treat the case where the scale has a natural middle category no differently from the case where the scale does not have a middle category. This paper proposes new models for the analysis of ordinal response scales with middle categories, applying these to data collected in 1993-1994 on American opinion toward the balance between environmental quality and economic prosperity. Some of the models should also be useful when the scale does not have a natural middle category. The models are easily used to address issues of concern in empirical work—for example, stochastic ordering among covariate classes and asymmetry about the middle category. Log-linear models are considered in Section 2. The relationship between the normal distribution and a quadratic log-linear model with known scores, discussed in this section, is the basis for Section 3, which considers a log-nonlinear model with unknown scores estimated from the data. Section 4 shows how generalized log-linear and generalized log-nonlinear models can be used to simultaneously study whether the response is below, at, or above the midpoint, and the conditional distribution of responses above (below) the midpoint. These models are also useful when the response scale is viewed as nested and/or the response process is sequential.     

Income tax evasion dynamics: Evidence from an agent-based econophysics model

We analyze income tax evasion dynamics in a standard model of statistical mechanics, the Ising model of ferromagnetism. However, in contrast to previous research, we use an inhomogeneous multi-dimensional Ising model where the local degrees of freedom (agents) are subject to a specific social temperature and coupled to external fields which govern their social behavior. This new modeling frame allows for analyzing large societies of four different and interacting agent types. As a second novelty, our model may reproduce results from agent-based models that incorporate standard Allingham and Sandmo tax evasion features as well as results from existing two-dimensional Ising based tax evasion models. In this way, such kind of models may become more relevant and useful in economics as well as social psychology. We finally use our model for analyzing income tax evasion dynamics under different enforcement scenarios and point to some policy implications that may also be of interest for psychological research on tax compliance.     

The Role of Ideas and Interests in Voluntary Sector Organizing: Some Insights from Canadian Scholarship

This article makes the case that a more sociological and discursive approach to nonprofit studies is needed to analyze sectoral dynamics. Using a sociological framework, it explores how the unique experiences and strategies of the nonprofit sectors are embedded in broader shifts in governance at a macro scale. Finally, it illustrates how Canadian scholarship provides a valuable lens that extends current theoretical frameworks by linking the analysis of sectoral mobilization and organization with the in-depth investigation of government–nonprofit relationships.     

Analyzing ethnoracial mobilization

This article examines scholarship about ethnoracial mobilization written by sociologists within the subfields of social movements and race and racism. We situate our synthesis within critiques put forward by other scholars about the treatment of ethnoracial movements within the social movement subfield. Using these critiques as launching points, we find two broad patterns in the literature: (a) a focus on ethnoracial social movements that decenters race, at times treating it as an independent variable and (b) a focus on mobilizations for racial equity that treats race as a dynamic and constructed process. Within the latter focus, we note research that investigates ethnoracial mobilization at the macro‐, meso‐, and micro‐levels. We call for more research on movements that specifically consider the mobilization and construction of ethnoracial identities. In doing so, we provide a conceptual map of the field and make suggestions for how social movement scholars employing distinct theoretical foci can engage in ethnoracial analysis. Finally, we hypothesize why there might be a dearth of research within the social movement subfield that engages in critical analysis of ethnoracial dynamics of social movements.     

Recent developments in exponential random graph (p*) models for social networks

This article reviews new specifications for exponential random graph models proposed by Snijders et al. [Snijders, T.A.B., Pattison, P., Robins, G.L., Handcock, M., 2006. New specifications for exponential random graph models. Sociological Methodology] and demonstrates their improvement over homogeneous Markov random graph models in fitting empirical network data. Not only do the new specifications show improvements in goodness of fit for various data sets, but they also help to avoid the problem of near-degeneracy that often afflicts the fitting of Markov random graph models in practice, particularly to network data exhibiting high levels of transitivity. The inclusion of a new higher order transitivity statistic allows estimation of parameters of exponential graph models for many (but not all) cases where it is impossible to estimate parameters of homogeneous Markov graph models. The new specifications were used to model a large number of classical small-scale network data sets and showed a dramatically better performance than Markov graph models. We also review three current programs for obtaining maximum likelihood estimates of model parameters and we compare these Monte Carlo maximum likelihood estimates with less accurate pseudo-likelihood estimates. Finally, we discuss whether homogeneous Markov random graph models may be superseded by the new specifications, and how additional elaborations may further improve model performance.     

Does greater product information actually inform consumer decisions? The relationship between product information quantity and diversity of consumer decisions

For many consumer goods, the advent of online markets dramatically increases the amount of information available about products’ different features and qualities. Although numerous studies have investigated the effects of information quantity on individual-level decisions, it is still unknown how the amount of attribute information affects group-level patterns of behavior, particularly when consumers are also aware of a choice’s popularity. In the present studies, we hypothesized that when attribute information increases, it may exceed the individual’s cognitive capacity to process this information, and as a result conformity - choosing the most popular item - becomes more likely. In this study, we first examined empirical data collected from human subject experiments in a simulated online shopping experience, and then developed an agent-based model (ABM) to explore this behavioral clustering. Both studies confirmed our primary hypotheses, and the ABM shows promise as a tool for exploring extensions of these ideas.     

Positive Emotions,Instrumental Resources,and Organizational Network Evolution: Theorizing via Simulation Research

In the workplace, people seek positive emotional experiences as well as instrumental resources while doing their work. Yet we know little about how affective micro-dynamics drive the evolution of organizational networks, influence network trajectories, and determine macro outcomes such as collective affect and overall network structure. Given the lack of theory on affective micro-dynamics and network evolution, we propose a model that includes both affective and instrumental micro-mechanisms and use simulation methods to explore evolutionary dynamics and develop new theory. The core of our model is the empirically observed tendency for people to forego the acquisition of instrumental resources to avoid a decrease in positive emotion when choosing interaction partners. We conduct “experiments” with the simulation, considering the effects of the tradeoff, dispositional affect, resource inequality, and ingroup favoritism. The results show that dispositional affect and the tradeoff have considerable effects on network trajectories, collective affect, and resource transfer. We provide new theoretical propositions about affect in organizations.     

Modelling the evolution of a bipartite network—Peer referral in interlocking directorates

A central part of relational ties between social actors is constituted by shared affiliations and events. The action of joint participation reinforces personal ties between social actors as well as mutually shared values and norms that in turn perpetuate the patterns of social action that define groups. Therefore the study of bipartite networks is central to social science. Furthermore, the dynamics of these processes suggests that bipartite networks should not be considered static structures but rather be studied over time. In order to model the evolution of bipartite networks empirically we introduce a class of models and a Bayesian inference scheme that extends previous stochastic actor-oriented models for unimodal graphs. Contemporary research on interlocking directorates provides an area of research in which it seems reasonable to apply the model. Specifically, we address the question of how tie formation, i.e. director recruitment, contributes to the structural properties of the interlocking directorate network. For boards of directors on the Stockholm stock exchange we propose that a prolific mechanism in tie formation is that of peer referral. The results indicate that such a mechanism is present, generating multiple interlocks between boards.     

The Food Crisis and its Impacts on Poverty in Senegal and Mali: Crossed Destinies

This article uses two CGE macro‐micro models to analyse the distributional impact of the food crisis and policy responses in two neighbouring African countries, both of which are strongly dependent on agriculture. The approach captures structural differences at both the macro and micro level for household income and expenditure structures, and the results reveal differences for poverty impact at the national and sub‐group levels, as well as for inequality and pro‐poor analysis. The importance of country‐specific analysis and the risk of extrapolating conclusions from one country to another are also highlighted.     

Clearing the FOG: Fuzzy,overlapping groups for social networks

Humans are well known to belong to many associative groups simultaneously, with various levels of affiliation. However, most group detection algorithms for social networks impose a strict partitioning on nodes, forcing entities to belong to a single group. Link analysis research has produced several methods which detect multiple memberships but force equal membership. This paper extends these approaches by introducing the FOG framework, a stochastic model and group detection algorithm for fuzzy, overlapping groups. We apply our algorithm to both link data and network data, where we use a random walk approach to generate rich links from networks. The results demonstrate that not only can fuzzy groups be located, but also that the strength of membership in a group and the fraction of individuals with exclusive membership are highly informative of emerging group dynamics.     

Migration in Namibia: Combining Macro and Micro Approaches to Research Design and Analysis1

In adopting a framework that applies both macro and micro variables to the study of migration in Namibia, the analysis of the findings of the Namibia Migration Project emphasizes the utility of combining different scales and methods of data collection in terms of explaining migration dynamics and extending trends and patterns to future scenarios. It is argued that the contextual and explanatory macro factors such as political history, poverty, population, environment, epidemics and culture are crucial to the understanding and interpretation of the micro data collected through the standardized questionnaire survey and the case study material. Equally important is the iterative relationship that the macro/micro approach fosters in the research design and in the analysis of the data between the macro and micro scales of investigation.     

Networks and collective action

This paper proposes a new measure for a group's ability to lead society to adopt their standard of behavior, which in particular takes account of the time the group takes to convince the whole society to adopt their position. This notion of a group's power to initiate action is computed as the reciprocal of the resistance against it, which is in turn given by the expected absorption time of a related finite state partial Markov chain that captures the social dynamics. The measure is applicable and meaningful in a variety of models where interaction between agents is formalized through (weighted) binary relations. Using Percolation Theory, it is shown that the group power is monotonic as a function of groups of agents. We also explain the differences between our measure and those discussed in the literature on Graph Theory, and illustrate all these concerns by a thorough analysis of two particular cases: the Wolfe Primate Data and the 11S hijackers’ network.     

Capitals, assets, and resources: some critical issues

This paper explores the potential of Bourdieu's approach to capital as a way of understanding class dynamics in contemporary capitalism. Recent rethinking of class analysis has sought to move beyond what Rosemary Crompton (1998) calls the 'employment aggregate approach', one which involves categorizing people into class groups according to whether they have certain attributes (e.g. occupations). Instead, recent contributions by Pierre Bourdieu, Erik Wright, Aage Sorensen, and Charles Tilly have concentrated on understanding the mechanisms that produce class inequalities. Concepts such as assets, capitals and resources (CARs) are often used to explain how class inequalities are produced, but there remain ambiguities and differences in how such terms are understood. This paper identifies problems faced both by game theoretical Marxism and by the rational choice approach of Goldthorpe in developing an adequate approach to CARs. It then turns to critically consider how elements of Bourdieu's approach, where his concept of capital is related to those of habitus and field, might overcome these weaknesses. Our rendering of his arguments leads us to conclude that our understanding of CARs might be enriched by considering how capital is distinctive not in terms of distinct relations of exploitation, but through its potential to accumulate and to be converted to other resources. This focus, we suggest, sidesteps otherwise intractable problems in CAR based approaches.     

Stochastic a posteriori blockmodels: Construction and assessment

In 1983, Holland, Laskey, and Leinhardt, using the ideas of Holland and Leinhardt, and Fienberg and Wasserman, introduced the notion of a stochastic blockmodel. The mathematics for stochastic a priori blockmodels, in which exogenous actor attribute data are used to partition actors independently of any statistical analysis of the available relational data, have been refined by several researchers and the resulting models used by many. Attempts to simultaneously partition actors and to perform relational data analyses using statistical methods that yield stochastic a posteriori blockmodels are still quite rare. In this paper, we discuss some old suggestions for producing such posterior blockmodels, and comment on other new suggestions based on multiple comparisons of model parameters, log-linear models for ordinal categorical data, and correspondence analysis. We also review measures for goodness-of-fit of a blockmodel, and we describe a natural approach to this problem using likelihood-ratio statistics generated from a popular model for relational data.     

Introduction to stochastic actor-based models for network dynamics

Stochastic actor-based models are models for network dynamics that can represent a wide variety of influences on network change, and allow to estimate parameters expressing such influences, and test corresponding hypotheses. The nodes in the network represent social actors, and the collection of ties represents a social relation. The assumptions posit that the network evolves as a stochastic process ‘driven by the actors’, i.e., the model lends itself especially for representing theories about how actors change their outgoing ties. The probabilities of tie changes are in part endogenously determined, i.e., as a function of the current network structure itself, and in part exogenously, as a function of characteristics of the nodes (‘actor covariates’) and of characteristics of pairs of nodes (‘dyadic covariates’). In an extended form, stochastic actor-based models can be used to analyze longitudinal data on social networks jointly with changing attributes of the actors: dynamics of networks and behavior.