Stochastic nonlinear dynamics of interpersonal and romantic relationships

Current theories from biosocial (e.g., the role of neurotransmitters in behavioral features), ecological (e.g., cultural, political, and institutional conditions), and interpersonal (e.g., attachment) perspectives have grounded interpersonal and romantic relationships in normative social experiences. However, these theories have not been developed to the point of providing a solid theoretical understanding of the dynamics present in interpersonal and romantic relationships, and integrative theories are still lacking. In this paper, mathematical models are used to investigate the dynamics of interpersonal and romantic relationships, via ordinary and stochastic differential equations, in order to provide insight into the behaviors of love. The analysis starts with a deterministic model and progresses to nonlinear stochastic models capturing the stochastic rates and factors (e.g., ecological factors, such as historical, cultural and community conditions) that affect proximal experiences and shape the patterns of relationship. Numerical examples are given to illustrate various dynamics of interpersonal and romantic behaviors with particular emphases placed on sustained oscillations and transitions between locally stable equilibria that are observable in stochastic models (closely related to real interpersonal dynamics), but absent in deterministic models.     

Public Attitudes Toward Nanotechnology

Data from 3909 respondents to an Internet survey questionnaire provide the first insights into public perceptions of nanotechnology. Quantitative analysis of statistics about agreement and disagreement with two statements, one positive and the other negative, reveals high levels of enthusiasm for the potential benefits of nanotechnology and little concern about possible dangers. The respondents mentally connect nanotechnology with the space program, nuclear power, and cloning research, but rate it more favorably. In contrast, they do not associate nanotechnology with pseudoscience, despite its imaginative exploitation by science fiction writers. Qualitative analysis of written comments from 598 respondents indicates that many ideas about the value of nanotechnology have entered popular culture, and it provides material for an additional 108 questionnaire items that can be used in future surveys on the topic. The findings of this exploratory study can serve as benchmarks against which to compare results of future research on the evolving status of nanotechnology in society.     

Something out of nothing: a Bayesian learning computational model for the social construction of value

ABSTRACT

This article develops a formalism for the social construction of value. Using a model based on Bayesian agents, it demonstrates how “something” arises out of “nothing” via the emergence of durable value conventions and shows how the developed framework can be used to investigate socially constructed valuations under a variety of circumstances. The resulting analysis clarifies why assumptions that collectives will converge upon the “intrinsic” (i.e., non-socially originating) value of an object (e.g., market efficiency) may not hold for mixed social and non-social valuation regimes, explains the dependency of socially constructed valuations on early accidents, demonstrates the effects of confident actors on constructed values, and identifies the production of time-dependent ratcheting effects from the interaction of bubbles with value conventions.     

Equality and Freedom as Uncertainty in Groups

In this paper, I investigate a connection between a common characterisation of freedom and how uncertainty is managed in a Bayesian hierarchical model. To do this, I consider a distributed factorization of a group’s optimization of free energy, in which each agent is attempting to align with the group and with its own model. I show how this can lead to equilibria for groups, defined by the capacity of the model being used, essentially how many different datasets it can handle. In particular, I show that there is a “sweet spot” in the capacity of a normal model in each agent’s decentralized optimization, and that this “sweet spot” corresponds to minimal free energy for the group. At the sweet spot, an agent can predict what the group will do and the group is not surprised by the agent. However, there is an asymmetry. A higher capacity model for an agent makes it harder for the individual to learn, as there are more parameters. Simultaneously, a higher capacity model for the group, implemented as a higher capacity model for each member agent, makes it easier for a group to integrate a new member. To optimize for a group of agents then requires one to make a trade-off in capacity, as each individual agent seeks to decrease capacity, but there is pressure from the group to increase capacity of all members. This pressure exists because as individual agent’s capacities are reduced, so too are their abilities to model other agents, and thereby to establish pro-social behavioural patterns. I then consider a basic two-level (dual process) Bayesian model of social reasoning and a set of three parameters of capacity that are required to implement such a model. Considering these three capacities as dependent elements in a free energy minimization for a group leads to a “sweet surface” in a three-dimensional space defining the triplet of parameters that each agent must use should they hope to minimize free energy as a group. Finally, I relate these three parameters to three notions of freedom and equality in human social organization, and postulate a correspondence between freedom and model capacity. That is, models with higher capacity, have more freedom as they can interact with more datasets.     

A statistical mechanics approach to Granovetter theory

In this paper we try to bridge breakthroughs in quantitative sociology/econometrics, pioneered during the last decades by Mac Fadden, Brock–Durlauf, Granovetter and Watts–Strogatz, by introducing a minimal model able to reproduce essentially all the features of social behavior highlighted by these authors.     

Sociocultural Meanings of Nanotechnology: Research Methodologies

This article identifies six social-science research methodologies that will be useful for charting the sociocultural meaning of nanotechnology: web-based questionnaires, vignette experiments, analysis of web linkages, recommender systems, quantitative content analysis, and qualitative textual analysis. Data from a range of sources are used to illustrate how the methods can delineate the intellectual content and institutional structure of the emerging nanotechnology culture. Such methods will make it possible in future to test hypotheses such as that there are two competing definitions of nanotechnology – the technical-scientific and the science-fiction – that are influencing public perceptions by different routes and in different directions.     

The importance of being systematically surprisable: Comparative social simulation as experimental technique

We argue that computer simulation can serve as a functional equivalent for the experimental method in sociology, with respect to theory development. To this end we present accounts of experimentation and simulation by experimenting/simulating scientists and sociologists of science. From these analyses we conclude desirable features of a simulation method: generality, surprisability and power to separate. That means that it should be widely applicable, capable of surprising the researcher, and capable of separating surprising results that originate from sociological features of the model from those that stem from technical features. We demonstrate three methods that may provide these features: emergence, fixing points of reference, and comparative response testing. We develop the latter method in greater depth by discussing an exemplary simulation study.     

The Role of Metanetworks in Network Evolution

The question of what structures of relations between actors emerge in the evolution of social networks is of fundamental sociological interest. The present research proposes that processes of network evolution can be usefully conceptualized in terms of a network of networks, or “metanetwork,” wherein networks that are one link manipulation away from one another are connected. Moreover, the geography of metanetworks has real effects on the course of network evolution. Specifically, both equilibrium and non-equilibrium networks located in more desirable regions of the metanetwork are found to be more probable. These effects of metanetwork geography are illustrated by two dynamic network models: one in which actors pursue access to unique information through “structural holes,” and the other in which actors pursue access to valid information by minimizing path length. Finally, I discuss future directions for modeling network dynamics in terms of metanetworks.     

Dissecting income segregation: Impacts of concentrated affluence on segregation of poverty

This article investigates how income inequality shapes residential segregation by income. Using agent-based modeling, it develops a residential preferences model that is capable of generating results mimicking empirical income segregation patterns. Simulation analysis shows how varying income inequality produces differential residential mobility outcomes that alter income segregation profiles. The model is used to capture the distinct impacts of households’ moves into richer or poorer neighborhoods, and how these impacts are further differentiated with respect to the moving household’s income. The article demonstrates how aggregating such diverse outcomes of micro-level interactions at a meso-level can help us to better understand the changes in macro-level income segregation patterns. Analyzing residential mobility patterns carefully, the article suggests that i) segregation of affluence and of poverty can trigger each other via initiating cascades of residential mobility and housing prices, and ii) increasing income inequality can disrupt housing market and lead to shortages in affordable housing, which can yield high residential instability and eviction rates among the poorest stratum.     

Mathematical analysis of crime dynamics in and out of prisons

Recently, there have been many attempts to develop a mathematical model that captures the nature of crime. One of the successful models has been based on diffusion‐type differential equations that describe how criminals spread in a specific area. Here, we propose a dynamic model that focuses on the effect of interactions between distinct types of criminals. The accumulated criminal records show that serious and minor crimes differ in many measures and are related in a complex way. While some of those who have committed minor crime spontaneously evolve into serious criminals, the transition from minor crime to major crime involves many social factors and has not been fully understood yet. In this work, we present a mathematical model to describe how minor criminals turn into major criminals inside and outside of prisons. The model assumes that a population can be divided into a set of compartments, according to the level of crime and whether arrested or not, and individuals have equal probability to change compartment. The model is design to implement two social effects which respectively have been conceptualized in popular terms “broken windows effect” and “prison as a crime school.” Analysis of the system shows how the crime‐related parameters such as the arrest rate, the period of imprisonment, and the in‐prison contact rate affect the criminal distribution at equilibrium. Without proper control of contact between prisoners, the longer imprisonment rather increases occurrence of serious crimes in society. An optimal allocation of the police resources to suppress crimes is also discussed.