Blau space

Abstract: This article develops and tests an evolutionary model of the growth, decline, and demographic dynamics of voluntary organizations. The model demonstrates a strong analogy between the adaptive landscape of Sewall Wright (1931) and the exploitation surfaces generated by a model of member selection and retention for voluntary associations. The article connects the processes of membership recruitment and loss to the social networks connecting individuals. The model generates dynamic hypotheses about the time path of organizations in sociodemographic dimensions. A key idea in this model is that membership selection processes at the individual level produce adaptation in communities of organizations. The article concludes with an empirical example and some discussion of the implications of the model for a variety of research literatures. Predicting the behavior of empirical systems has proven to be an elusive goal for the social sciences. This article outlines a theory that predicts the growth, decline, and demographic changes of social groups. The theory posits a Darwinian mechanism of systematic variation, selection, and retention of members in groups. Social network theory provides a framework for understanding how social evolution (the transitions from hunting and gathering societies through the intervening stages to the contemporary industrial stage) has created the conditions for the Darwinian mechanism of the model. This study takes a brief tour through the macroevolutionary foundations of the theory to set the stage for the microevolutionary test of the model. The predictions tested in the article are in the short term - over a period of less * Work on this article was supported by National Science Foundation grants SES-8120666, SES

Abstract: Miller McPherson's approach to measuring the inherent duality of organizational forms and the environmental niches that they occupy is adapted and applied to an analysis of the institutional field of (outdoor) poverty relief organizations operating in New York City (1888–1917). In contrast to McPherson's approach that emphasizes how organizations are differentially arrayed within “Blau space,” this chapter focuses on how organizational forms are distributed across an institutional “logic space” that is itself dually ordered and defined by the kinds of organizational forms that are understood to exist. The resulting niche maps are employed to trace out the jurisdictional conflicts that erupted during the Progressive Era between two competing organizational forms – scientific charities and settlement houses – each of which embodied a particular vision and practice for delivering social relief to the poor.

Abstract: We develop a method of imputing ego network characteristics for respondents in probability samples of individuals. This imputed network uses the homophily principle to estimate certain properties o...

Abstract: McPherson's Blau space and affiliation ecology model is a powerful tool for analyzing the ecological competition among social entities, such as organizations, along a combination of sociodemographic characteristics of their members. In this paper we introduce the R-based Graphical User Interface (GUI) package Blaunet, an integrated set of tools to calculate, visualize, and analyze the statuses of individuals and social entities in Blau space, parameterized by multiple sociodemographic traits as dimensions. The package is able to calculate the Blau statuses at the nodal, dyadic, and meso levels based on three types of information: sociodemographic characteristics, group affiliations (e.g., membership in groups/organizations), and network ties. To facilitate this, Blaunet has the following five main capabilities, it can: 1) identify a list of possible salient dimensions; 2) calculate, plot, and analyze niches for social entities by measuring the social distance along the salient dimensions between individuals affiliated with them; 3) generate Blau bubbles for individuals, thereby allowing the study of interpersonal influence of similar others even with limited or no network information; 4) capture niche dynamics cross-sectionally by calculating the intensity of exploitation from the carrying capacity and the membership rate; and 5) analyze the niche movement longitudinally by estimating the predicted niche movement equations. We illustrate these capabilities of Blaunet with example datasets.