Model Of Social Dynamics Research Articles

Effect of external fields in Axelrod's model of social dynamics

The study of the effects of spatially uniform fields on the steady-state properties of Axelrod's model has yielded plenty of counterintuitive results. Here, we reexamine the impact of this type of field for a selection of parameters such that the field-free steady state of the model is heterogeneous or multicultural. Analyses of both one- and two-dimensional versions of Axelrod's model indicate that the steady state remains heterogeneous regardless of the value of the field strength. Turning on the field leads to a discontinuous decrease on the number of cultural domains, which we argue is due to the instability of zero-field heterogeneous absorbing configurations. We find, however, that spatially nonuniform fields that implement a consensus rule among the neighborhood of the agents enforce homogenization. Although the overall effects of the fields are essentially the same irrespective of the dimensionality of the model, we argue that the dimensionality has a significant impact on the stability of the field-free homogeneous steady state.

Adversarial scheduling in discrete models of social dynamics

In this paper we advocate the study of discrete models of social dynamics underadversarial scheduling. The approach we propose forms part of a foundational basis for agenerative approach to social science(Epstein 2007). We highlight the feasibility of the adversarial scheduling approach by using it to study thePrisoners's Dilemma Game with Pavlov update, a dynamics that has already been investigated under random update in Kittock (1994), Dyeret al. (2002), Mossel and Roch (2006) and Dyer and Velumailum (2011). The model is specified by letting players at the nodes of an underlying graphGrepeatedly play the Prisoner's Dilemma against their neighbours. The players adapt their strategies based on the past behaviour of their opponents by applying the so-called win–stay lose–shift strategy. With random scheduling, starting from any initial configuration, the system reaches the fixed point in which all players cooperate with high probability. On the other hand, under adversarial scheduling the following results hold:—A scheduler that can selectbothgame participants can preclude the system from reaching the unique fixed point on most graph topologies.—A non-adaptive scheduler that is only allowed to chooseoneof the participants is no more powerful than a random scheduler. With this restriction, even an adaptive scheduler is not significantly more powerful than the random scheduler, provided it is ‘reasonably fair’.

Influence of network dynamics on the spread of sexually transmitted diseases

Network epidemiology often assumes that the relationships defining the social network of a population are static. The dynamics of relationships is only taken indirectly into account by assuming that the relevant information to study epidemic spread is encoded in the network obtained, by considering numbers of partners accumulated over periods of time roughly proportional to the infectious period of the disease. On the other hand, models explicitly including social dynamics are often too schematic to provide a reasonable representation of a real population, or so detailed that no general conclusions can be drawn from them. Here, we present a model of social dynamics that is general enough so its parameters can be obtained by fitting data from surveys about sexual behaviour, but that can still be studied analytically, using mean-field techniques. This allows us to obtain some general results about epidemic spreading. We show that using accumulated network data to estimate the static epidemic threshold lead to a significant underestimation of that threshold. We also show that, for a dynamic network, the relative epidemic threshold is an increasing function of the infectious period of the disease, implying that the static value is a lower bound to the real threshold. A practical example is given of how to apply the model to the study of a real population.

Resolution of the Stochastic Strategy Spatial Prisoner's Dilemma by Means of Particle Swarm Optimization

We study the evolution of cooperation among selfish individuals in the stochastic strategy spatial prisoner's dilemma game. We equip players with the particle swarm optimization technique, and find that it may lead to highly cooperative states even if the temptations to defect are strong. The concept of particle swarm optimization was originally introduced within a simple model of social dynamics that can describe the formation of a swarm, i.e., analogous to a swarm of bees searching for a food source. Essentially, particle swarm optimization foresees changes in the velocity profile of each player, such that the best locations are targeted and eventually occupied. In our case, each player keeps track of the highest payoff attained within a local topological neighborhood and its individual highest payoff. Thus, players make use of their own memory that keeps score of the most profitable strategy in previous actions, as well as use of the knowledge gained by the swarm as a whole, to find the best available strategy for themselves and the society. Following extensive simulations of this setup, we find a significant increase in the level of cooperation for a wide range of parameters, and also a full resolution of the prisoner's dilemma. We also demonstrate extreme efficiency of the optimization algorithm when dealing with environments that strongly favor the proliferation of defection, which in turn suggests that swarming could be an important phenomenon by means of which cooperation can be sustained even under highly unfavorable conditions. We thus present an alternative way of understanding the evolution of cooperative behavior and its ubiquitous presence in nature, and we hope that this study will be inspirational for future efforts aimed in this direction.

Comparing Agent-Based Computational Simulation Models in Cross-Cultural Research

Mel Ember was co-Principal Investigator in the Mason-HRAF Joint Project on Eastern Africa, a multiyear project aimed at developing and analyzing advanced computational agent-based models of human societies across 10 countries and 12 ecosystems. A major unsolved challenge in this kind of social science research is to devise a systematic way to compare, contrast, and communicate different models of social dynamics along relevant dimensions and characteristics, given the inherent complexity of most computational agent-based models. This article proposes a viable systematic framework for comparing models and illustrates its application using some of the models that Mel helped inspire and develop as senior project participant.

Affinity Paths and information diffusion in social networks

Widespread interest in the diffusion of information through social networks has produced a large number of Social Dynamics models. A majority of them use theoretical hypothesis to explain their diffusion mechanisms while the few empirically based ones average out their measures over many messages of different contents. Our empirical research tracking the step-by-step email propagation of an invariable viral marketing message delves into the content impact and has discovered new and striking features. The topology and dynamics of the propagation cascades display patterns not inherited from the email networks carrying the message. Their disconnected, low transitivity, tree-like cascades present positive correlation between their nodes probability to forward the message and the average number of neighbors they target and show increased participants’ involvement as the propagation paths length grows. Such patterns not described before, nor replicated by any of the existing models of information diffusion, can be explained if participants make their pass-along decisions based uniquely on local knowledge of their network neighbors affinity with the message content. We prove the plausibility of such mechanism through a stylized, agent-based model that replicates the Affinity Paths observed in real information diffusion cascades.

Scope and Limits of Predictions by Social Dynamic Models: Crisis, Innovation, Decision Making

Models of social dynamics grew up from methods used on the physical and life sciences. Different types of growth processes, among others those which lead to finite time singularities are reviewed. The scope and limits of the analogy between neurological disorders (such as epilepsy) and financial crises are analyzed by exploiting the concept of dynamical diseases. Governing rules of social dynamics phenomena can be extracted from collected empirical data, and they have some predictive power. Data for evolving patent citation networks, and annual budget changes are analyzed for generating dynamical models.

The Cognitive integrators: an Experience of the Modeling of Social Dynamics

The Cognitive integrators: an Experience of the Modeling of Social Dynamics

Public Security vs. Private Self-Protection: Optimal Taxation and the Social Dynamics of Fear

In this paper, we develop a simple model of social dynamics governing the evolution of strategic self-protection choices of boundedly rational potential victims facing the threat of prospective offenders in a large population with random matching. We prove that individual (and socially transmitted) fear of exposure to criminal threats may actually condition choices even in the face of objective evidence of declining crime rates, and thereby cause the eventual selection of Pareto inefficient equilibria with self-protection. We also show that a suitable strategy of provision of public security financed through discriminatory taxation of self-protective expenses may actually overcome this problem, and drive the social dynamics toward the efficient no protection equilibrium. In our model, we do not obtain, as in Cressman et al. (1998), a crowding-out result such that the net impact of public spending on the actual social dynamics is neutral and the economy keeps on cycling between phases of high and low criminal activity with varying levels of self-protection; quite to the contrary, it can be extremely effective in implementing the social optimum, in that it acts primarily on the intangible dimension, that is, on the social dynamics of fear. We claim that this kind of result calls for more interdisciplinary research on the socio-psycho-economic determinants of fear of crime, and for consequent advances in modelling approaches and techniques.

Quantum Modeling of Social Dynamics

In this paper, the authors apply models extracted from the Many-Body Quantum Mechanics to understand how knowledge production is correlated to the innovation potential of a work team. This study is grounded in key assumtpions. First, complexity theory applied to social science suggests that it is of paramount importance to consider elements of non-objectivity and non-determinism in the statistical description of socio-economic phenomena. Second, a typical factor of indeterminacy in the explanation of these phenomena lead to the need to apply the instruments of quantum physics to formally describe social behaviours. In order to experiment the validity of the proposed mathematic model, the research intends to: 1) model nodes and interactions; 2) simulate the network behaviour starting from specific defined models; 3) visualize the macroscopic results emerging during the analysis/simulation phases through a digital representation of the social network.

Sensitivity to noise variance in a social network dynamics model

Abstract : The dynamics of social networks are modeled with a system of continuous Stochastic Ordinary Differential Equations (SODE). With the proper amount of noise input, the SODE model captures dynamic features that are lacking in the corresponding deterministic ODE model. Therefore, sensitivity to noise levels is investigated by considering four different regimes: essentially deterministic, noise-enriched, noise-enlarged, and noise-dominated. Each regime is defined based on the behavior of solutions of the SODE, and the geometry of the regimes is categorized with stochastic simulations.

Risk Factors and Consequences of Unwanted Sex Among University Students

This is the first study of unwanted sexual experiences in the collegiate "hooking-up" culture. In a representative sample of 178 students at a small liberal arts university. Twenty-three percent of women and 7% of men surveyed reported one or more experiences of unwanted sexual intercourse. Seventy-eight percent of unwanted vaginal, anal, and oral incidents took place while--"hooking up,"--whereas 78% of unwanted fondling incidents occurred at parties or bars. The most frequently endorsed reason for unwanted sexual intercourse was impaired judgment due to alcohol. The most frequently endorsed reason for unwanted fondling was that it happened before the perpetrator could be stopped. Of those affected by unwanted sexual intercourse or unwanted fondling, 46.7% and 19.2% reported unwanted memories, 50% and 32.7% reported avoidance and numbing responses, and 30% and 26.9% reported hyperarousal responses, respectively. A preliminary model of unwanted sex and collegiate social dynamics is proposed to provide a heuristic for further research.

Intracultural diversity in a model of social dynamics

We study the consequences of introducing individual nonconformity in social interactions, based on Axelrod's model for the dissemination of culture. A constraint on the number of situations in which interaction may take place is introduced in order to lift the unavoidable homogeneity present in the final configurations arising in Axelrod's related models. The inclusion of this constraint leads to the occurrence of complex patterns of intracultural diversity whose statistical properties and spatial distribution are characterized by means of the concepts of cultural affinity and cultural cline. It is found that the relevant quantity that determines the properties of intracultural diversity is given by the fraction of cultural features that characterizes the cultural nonconformity of individuals.

Multi-agent models of social dynamics in children

A series of computational models are presented which address the question of how peer relations change over time. We examine data from a standardized metric (CDC) that places school children in one of five categories: Popular, Rejected, Neglected, Controversial, and Average, and how such classifications change over time. A simple random model is shown to not match the empirical data, while a computational model and an ACT-R model are shown to match equally well, even though they are highly architecturally distinct. To test these models’ ability to give useful predictions in other domains, we introduced variation among the individuals in the models. For both models, we observed equivalent behavior that was consistent with the empirically known effects of Hostile Attribution Bias, variations in social skill, and shyness among others. This indicates that both models are capturing inherent underlying regularities of the social dynamics of peer relations in children. The relationship between these models and its implications are discussed.

Conservation laws and asymptotic behavior of a model of social dynamics

A conservative social dynamics model is developed within a discrete kinetic framework for active particles, which has been proposed in [M.L. Bertotti, L. Delitala, From discrete kinetic and stochastic game theory to modelling complex systems in applied sciences, Math. Mod. Meth. Appl. Sci. 14 (2004) 1061–1084]. The model concerns a society in which individuals, distinguished by a scalar variable (the activity) which expresses their social state, undergo competitive and/or cooperative interactions. The evolution of the discrete probability distribution over the social state is described by a system of nonlinear ordinary differential equations. The asymptotic trend of their solutions is investigated both analytically and computationally. Existence, stability and attractivity of certain equilibria are proved.

Phenomenological models of socioeconomic network dynamics

We study a general set of models of social network evolution and dynamics. The models consist of both a dynamics on the network and evolution of the network. Links are formed preferentially between "similar" nodes, where the similarity is defined by the particular process taking place on the network. The interplay between the two processes produces phase transitions and hysteresis, as seen using numerical simulations for three specific processes. We obtain analytic results using mean-field approximations, and for a particular case we derive an exact solution for the network. In common with real-world social networks, we find coexistence of high and low connectivity phases and history dependence.

Local versus global interactions in nonequilibrium transitions: A model of social dynamics

A nonequilibrium system of locally interacting elements in a lattice with an absorbing order-disorder phase transition is studied under the effect of additional interacting fields. These fields are shown to produce interesting effects in the collective behavior of this system. Both for autonomous and external fields, disorder grows in the system when the probability of the elements to interact with the field is increased. There exists a threshold value of this probability beyond which the system is always disordered. The domain of parameters of the ordered regime is larger for nonuniform local fields than for spatially uniform fields. However, the zero field limit is discontinous. In the limit of vanishingly small probability of interaction with the field, autonomous or external fields are able to order a system that would fall in a disordered phase under local interactions of the elements alone. We consider different types of fields which are interpreted as forms of mass media acting on a social system in the context of Axelrod's model for cultural dissemination.

Strategies of voting in stochastic environment: Egoism and collectivism

Consideration was given to a model of social dynamics controlled by successive collective decisions based on the threshold majority procedures. The current system state is characterized by the vector of participants’ capitals (utilities). At each step, the voters can either retain their status quo or accept the proposal which is a vector of the algebraic increments in the capitals of the participants. In this version of the model, the vector is generated stochastically. Comparative utility of two social attitudes—egoism and collectivism—was analyzed. It was established that, except for some special cases, the collectivists have advantages, which makes realizable the following scenario: on the conditions of protecting the corporate interests, a group is created which is joined then by the egoists attracted by its achievements. At that, group egoism approaches altruism. Additionally, one of the considered variants of collectivism handicaps manipulation of voting by the organizers.

On the qualitative analysis of the solutions of a mathematical model of social dynamics

This work deals with a family of dynamical systems which were introduced in [M.L. Bertotti, M. Delitala, From discrete kinetic and stochastic game theory to modelling complex systems in applied sciences, Math. Models Methods Appl. Sci. 7 (2004) 1061–1084], modelling the evolution of a population of interacting individuals, distinguished by their social state. The existence of certain uniform distribution equilibria is proved and the asymptotic trend is investigated.

The social dynamics of diverse and closed networks

Inherent to the dynamics of social networks is a paradoxical trade-off between closed networks that promote cooperation and efficiency and diverse networks that are flexible to new resources and ideas. Since actors cannot simultaneously maximize both facets of a network, this has created a sharp debate on the social capital performance of closed and diverse network relationships. Research on this social capital debate has often focused on these described network affects without explaining the origins and dynamics of network performance. This paper advances a cognitive diversity approach that is based upon the subjective and alert behaviors of Austrian entrepreneurs. These are key causal drivers to this paper's theoretical model of social dynamics and performance of closed and diverse networks. Such network behavior is subsequently modeled as a Complex Adaptive system. Using agent-based simulation, an agent-based model of entrepreneurship and social network dynamics is constructed to test the relationships described by the proposed theoretical model. The simulation results support the described hypothesized relationships. These findings also suggest the benefits of closed and diverse networks are logically distinct and, thus, should not be viewed as an either-or phenomenon. Agent-based simulation results show entrepreneurs can construct a balanced network of closed and diverse networks to optimize the benefits of both networks.