The checkerboard model of social interaction

The checkerboard model is a computer simulation of social interaction among members of two groups. The checkerboard represents a social field on which two groups of checkers move on the board on the basis of positive, neutral or negative attitudes toward one another assigned to them. The resulting pattern of positions of the pieces represents the social structure. The theoretical basis for the checkerboard model is explained and the rules for operating the model are outlined. This is followed by illustrative runs named Crossroads, Mutual Suspicion, Segregation, Social Climber, Social Worker, Boy‐Girl, Couples and Husband‐Wives, showing intermediate and final positions on the board for each. It is concluded that the checkerboard model is capable of demonstrating the intimate connection between attitudes of group members toward their own group and toward others to a continuous social interactional process and to the resulting social structure.     

Disambiguating verbal comments in social interaction: A computer model of meaning*

This paper describes a computer program for disambiguating the meaning of verbal acts in social interaction. The program was implemented using artificial intelligence techniques, representing utterances by frames, designing separate agents employing procedural rules to infer values of each coding dimension, and sharing information through a blackboard. The computer algorithms are discussed and illustrated with examples, then results of an empirical test of the program are reported. A training‐set of verbal interactions among health care practitioners and patients was examined to build the program, with an independent test‐set of data used to assess program performance. On a practical level, this program offers a promising approach for computer‐assisted or even automatic coding of interaction processes, reducing coding costs and improving reliability and validity. On a theoretical level, these algorithms offer a model of how individuals disambiguate the meaning of verbal comments in social interaction, providing substantive insights into the mechanisms of social interaction.     

Inferring social structure from continuous‐time interaction data

Relational event data, which consist of events involving pairs of actors over time, are now commonly available at the finest of temporal resolutions. Existing continuous‐time methods for modeling such data are based on point processes and directly model interaction “contagion,” whereby one interaction increases the propensity of future interactions among actors, often as dictated by some latent variable structure. In this article, we present an alternative approach to using temporal‐relational point process models for continuous‐time event data. We characterize interactions between a pair of actors as either spurious or as resulting from an underlying, persistent connection in a latent social network. We argue that consistent deviations from expected behavior, rather than solely high frequency counts, are crucial for identifying well‐established underlying social relationships. This study aims to explore these latent network structures in two contexts: one comprising of college students and another involving barn swallows.     

Multi-attribute decision-making in individual and social choice

This paper provides an analysis of individual and social decision criteria for alternatives that are composed of several attributes. We derive additive and multiplicative criteria for individual decision-making with new axioms and apply these criteria to obtain new justifications of known social choice rules with a bargaining interpretation, namely the generalized utilitarian and Nash social choice functions. Unlike most axiomatizations of bargaining solutions, our approach is, to a large extent, based on the multi-attribute structure of the underlying alternatives and the resulting individual decision criteria instead of axioms that impose restrictions on the choice function directly.     

Stochastic optimization on social networks with application to service pricing

In this paper we develop a combined simulation and optimization approach for solving difficult decision problems on complex dynamic networks. For a specific reference problem we consider a telecommunication service provider who offers a telecommunication service to a market with network effects. More particularly, the service consumption of an individual user depends on both idiosyncratic characteristics and the popularity of this service among the customer’s immediate neighborhood. Both the social network and the individual user preferences are largely heterogeneous and changing over time. In addition the service provider’s decisions are made in absence of perfect knowledge about user preferences. The service provider pursues the strategy of stimulating the demand by offering differentiated prices to the customers. For finding the optimal pricing we apply a stochastic quasi-gradient algorithm that is integrated with a simulation model that drives the evolution of the network and user preferences over time. We show that exploiting the social network structure and implementing differentiated pricing can substantially increase the revenues of a service provider operating on a social network. More generally, we show that stochastic gradient methods represent a powerful methodology for the optimization of decisions in social networks.     

The social diversification of fashion

We propose a model to investigate the dynamics of fashion traits purely driven by social interactions. We assume that people adapt their style to maximize social success, and we describe the interaction as a repeated group game in which the payoffs reflect the social norms dictated by fashion. On one hand, the tendency to imitate the trendy stereotypes opposed to the tendency to diverge from them to proclaim identity; on the other hand, the exploitation of sex appeal for dating success opposed to the moral principles of the society. These opposing forces promote diversity in fashion traits, as predicted by the modeling framework of adaptive dynamics. Our results link the so-called horizontal dynamics—the primary driver of fashion evolution, compared with the vertical dynamics accounting for interclass and economic drivers—to style variety.     

Models of social contagion

This paper argues that social choice from among more than two feasible alternatives should not be based on social choice from two‐alternative subsets. It considers in some detail the case where one alternative ties or beats every other alternative on the basis of simple majorities, and raises the question of whether such an alternative should be chosen. A condition of ‘stochastic unanimity’, introduced in this context, is shown to be incompatible with the simple majority rule when it can apply. This new condition plus a consideration of ties leads into a brief discussion of the use of individual expected utility in social choice theory.     

Evolution of shame as an adaptation to social punishment and its contribution to social cohesiveness

Feelings of shame are common among humans although shameless individuals do not seem to be handicapped in achieving social success in life. What then is the adaptive value of shame? How can shame have evolved? Here I simulate shame as the emotion that induces an increase in pro‐social behavior after receiving social punishment. Simulations with the agent‐based model, Sociodynamica, show that shame is evolutionary stable in a context of individual selection, without the need for including group selection as an evolutionary force. The adaptive advantage of shame is based on the fact that it increases flexibility to the shameful individual, allowing it to act selfish if the probabilities of being punished are low and achieving a reduction in the costs of social punishment when frequent punishment is likely. The results show that shame, together with pro‐social punishment and social cooperation, produce a fluctuating dynamics of social cooperation, achieving long periods where the populations stabilizes pro‐social behavior interspersed with periods where selfish behavior predominates. This temporal stabilization of pro‐social behavior might provide societies with sufficient time to build institutions that might stabilize sustainable pro‐social behavior. © 2008 Wiley Periodicals, Inc. Complexity, 2008     

Constitutional implementation of social choice correspondences

A game form constitutionally implements a social choice correspondence if it implements it in Nash equilibrium and, moreover, the associated effectivity functions coincide. This paper presents necessary and sufficient conditions for a unanimous social choice correspondence to be constitutionally implementable, and sufficient and almost necessary conditions for an arbitrary (but surjective) social choice correspondence to be constitutionally implementable. It is shown that the results apply to interesting classes of scoring and veto social choice correspondences.     

Modeling the influence of social networks and environment on energy balance and obesity

The influence of social networks on the development of obesity has been demonstrated, and several models have been proposed. However, these models are limited since they consider obesity as a ‘contagious’ phenomenon that can be caught if most social contacts are deemed obese. Furthermore, social networks were proposed as a means to mitigate the obesity epidemic, but the interaction of social networks with environmental factors could not yet be explored as it was not accounted for in the current models. We propose a new model of obesity to face these limitations. In our model, individuals influence each other with respect to food intake and physical activity, which may lead to changes depending on the environment, and will impact energy balance and weight. We illustrate the potential of our model via two questions: could we focus on social networks and neglect environmental sources of influence, at least from a modelling viewpoint? Are some social structures less prone to be influenced by their environment? We performed a factorial analysis based on both synthetic and real-world social networks, and found that the environment was a key component behind changes in weight but its contribution was mitigated by structural properties of the population. Furthermore, the contribution of the environment was not dictated by macro-level properties (small-world and scale-free), which suggests that particular patterns of social ties at the micro-level are involved in making populations more resilient to change and less influenced by the environment.     

A Markov chain model for Asch‐type experiments†

A 4‐state absorbing Markov chain is advanced as a model to represent the response process of subjects in Asch‐type experiments. Both the development of the model and the estimation of its parameters are described. The model is applied to the responses obtained from subjects in an Asch‐type experiment designed to analyze the effects of prior status expectations on social influence.     

Deliberative mathematics education for social democratization in Latin America

Latin America is committed to build more democratic social relationships as a part of its current democratization process. Mathematics education is a relevant set of social practices that could contribute to the consolidation of democratic social relationships in the school. This dimension of social interaction in mathematics education as a source of democratization is explored conceptually and is given a practical meaning through the discussion of an inservice teacher education program, which illustrates a deliberative democratic ideology of mathematics education.     

Interacting neural networks and the emergence of social structure

The article describes a computational model for the simulation of the emergence of social structure or social order, respectively. The model is theoretically based on the theory of social typifying by Berger and Luckmann. It consists of interacting artificial actors (agents), which are represented by two neural networks, an action net, and a perception net. By mutually adjusting of their actions, the agents are able to constitute a self‐organized social order in dependency of their personal characteristics and certain features of their environment. A fictitious example demonstrates the applicability of the model to problems of extra‐terrestrial robotics. © 2007 Wiley Periodicals, Inc. Complexity 12: 41–52, 2007     

The value of cognitive foundations for dynamic social theory

It is possible to develop models of social behavior that are predicated on detailed mechanical models of cognition. Cognitively based social models are potentially unified theoretical frameworks that can be used to explain a wide variety of social phenomena. Moreover, if a knowledge representation scheme and a knowledge acquisition scheme are specified in the underlying cognitive model then it is possible to produce a dynamic social model. The resulting social model can thus be used to predict and explain not only conditions for specific behaviors but changes in those behaviors over time.

Constructuralism is a theory of social behavior that rests on a cognitive model. The cognitive model specified has a knowledge representation scheme, knowledge acquisition procedures, and control procedures for shifting cognitive attention. The resulting social model is a dynamic model that can be used to explain both conditions for the occurrence of a behavior and social and individual changes that accrue do to a series of behaviors. The explanatory breadth of the model is illustrated by looking at predictions about a variety of social phenomena including: development of shared knowledge, identical behavior by members of the society, foreign language acquisition, clique formation, civil disobedience, and diffusion of innovative information.     

A social perspective on technology-enhanced mathematical learning: from collaboration to performance

This paper documents both developments in the technologies used to promote learning mathematics and the influence on research of social theories of learning, through reference to the activities of the International Commission on Mathematical Instruction (ICMI), and argues that these changes provide opportunity for the reconceptualization of our understanding of mathematical learning. Firstly, changes in technology are traced from discipline-specific computer-based software through to Web 2.0-based learning tools. Secondly, the increasing influence of social theories of learning on mathematics education research is reviewed by examining the prevalence of papers and presentations, which acknowledge the role of social interaction in learning, at ICMI conferences over the past 20 years. Finally, it is argued that the confluence of these developments means that it is necessary to re-examine what it means to learn and do mathematics and proposes that it is now possible to view learning mathematics as an activity that is performed rather than passively acquired.     

The complexity of social groups and social systems described by graph structures

This paper presents methodology which permits the complete ranking of nondirected graphs (NDG's) on an attribute labelled ‘complexity.’ The technique applies to both small and large systems as might arise in studies of group or organization behavior. The methodology extends to cover the complexity of directed graphs (DG's) and permits the detailed specification of individual and group behavior.For the NDG an abstract automaton representing the participants' interaction or communications function is sited at each node. Each automaton is constructed so its internal complexity is sufficient to realize the minimal social action (e.g. transmission of a rumor and the path followed by the rumor) within the framework of the NDG. It is shown that the complexity of each node automaton depends upon the order of the graph, the degree of the node and the longest path parameter of the graph. The combined complexity of node automata constitutes the complexity of the NDG. The complexity of a DG is specified as a composition of complexities computed for the associated NDG and logical devices which produce the observed behavior. Illustrative examples pertaining to the committee-subcommittee problem and to organizational structures are presented.     

Knowledge management in semantic social networks

An object-oriented model of semantic social networks is proposed and formally analyzed. Methods for project, role, and team management based on the semantic model are defined and implemented in $\mathfrak{n}\mathfrak{i}\mathbf{K}\mathfrak{l}\mathfrak{a}\mathfrak{s}$ , a semantic wiki language based on frame logic developed by the author. The new approach to semantic social networks allows dynamic change of social network semantics and the establishment of the well known fishnet organization in a social network. In the end possible applications to knowledge management are presented.     

Nothing else matters: Evolution of preference for social prestige

This paper seeks answers to two questions. First, if a greater social activity of an individual enhances oblique (i.e. to non-relatives) transmission of her cultural traits at the expense of vertical (i.e. to children) transmission as well as family size, which behavior is optimal from cultural evolution standpoint? I formalize a general model that characterizes evolutionarily stable social activity. The proposed model replicates the theory of Newson et al. (2007) that fertility decline is caused by increasing role of oblique cultural transmission. Second, if social activity is a rational choice rather than a culturally inherited trait, and if cultural transmission acts on preferences rather than behaviors, which preferences survive the process of cultural evolution? I arrive at a very simple yet powerful result: under mild assumptions on model structure, only preferences which emphasize exclusively the concern for social prestige, i.e. extent to which one’s cultural trait has been picked up by others, survive.     

Disambiguation of social polarization concepts and measures

This article distinguishes nine senses of polarization and provides formal measures for each one to refine the methodology used to describe polarization in distributions of attitudes. Each distinct concept is explained through a definition, formal measures, examples, and references. We then apply these measures to GSS data regarding political views, opinions on abortion, and religiosity—topics described as revealing social polarization. Previous breakdowns of polarization include domain-specific assumptions and focus on a subset of the distribution’s features. This has conflated multiple, independent features of attitude distributions. The current work aims to extract the distinct senses of polarization and demonstrate that by becoming clearer on these distinctions we can better focus our efforts on substantive issues in social phenomena.     

The asymptotic behaviors of a stochastic social epidemics model with multi-perturbation

Alcohol abuse is a major social problem, which is often called social epidemic, for the some similarities to the classical infectious diseases. In this paper, we formulated a new stochastic alcoholism model based on the deterministic model proposed in \cite{Wangxy}, with the mortalities of all populations as well as the contact infected coefficient are all perturbed. Based on this model, we investigate the long-term stochastic dynamics behaviors of two equilibria of the corresponding deterministic model and point out the effect of random disturbance on the stability of the system. Finally, we carry out numerical simulations to support our theoretical results.