Who Replaces Whom? Local versus Non-local Replacement in Social and Evolutionary Dynamics

In this paper, we inspect well-known population genetics and social dynamics models. In these models, interacting individuals, while participating in a self-organizing process, give rise to the emergence of complex behaviors and patterns. While one main focus in population genetics is on the adaptive behavior of a population, social dynamics is more often concerned with the splitting of a connected array of individuals into a state of global polarization, that is, the emergence of speciation. Applying computational and mathematical tools we show that the way the mechanisms of selection, interaction and replacement are constrained and combined in the modeling have an important bearing on both adaptation and the emergence of speciation. Differently (un)constraining the mechanism of individual replacement provides the conditions required for either speciation or adaptation, since these features appear as two opposing phenomena, not achieved by one and the same model. Even though natural selection, operating as an external, environmental mechanism, is neither necessary nor sufficient for the creation of speciation, our modeling exercises highlight the important role played by natural selection in the interplay of the evolutionary and the self-organization modeling methodologies.Comment: 14 pages, 11 figure

Opinion Polarization by Learning from Social Feedback

We explore a new mechanism to explain polarization phenomena in opinion dynamics in which agents evaluate alternative views on the basis of the social feedback obtained on expressing them. High support of the favored opinion in the social environment, is treated as a positive feedback which reinforces the value associated to this opinion. In connected networks of sufficiently high modularity, different groups of agents can form strong convictions of competing opinions. Linking the social feedback process to standard equilibrium concepts we analytically characterize sufficient conditions for the stability of bi-polarization. While previous models have emphasized the polarization effects of deliberative argument-based communication, our model highlights an affective experience-based route to polarization, without assumptions about negative influence or bounded confidence.Comment: Presented at the Social Simulation Conference (Dublin 2017

Aggregation and Emergence in Agent-Based Models: A Markov Chain Approach

We analyze the dynamics of agent--based models (ABMs) from a Markovian perspective and derive explicit statements about the possibility of linking a microscopic agent model to the dynamical processes of macroscopic observables that are useful for a precise understanding of the model dynamics. In this way the dynamics of collective variables may be studied, and a description of macro dynamics as emergent properties of micro dynamics, in particular during transient times, is possible.Comment: 5 pages, 1 figur

Opinion Dynamics and Communication Networks

This paper examines the interplay of opinion exchange dynamics and communication network formation. An opinion formation procedure is introduced which is based on an abstract representation of opinions as k-dimensional bitstrings. Individuals interact if the difference in the opinion strings is below a defined similarity threshold dI. Depending on dI, different behaviour of the population is observed: low values result in a state of highly fragmented opinions and higher values yield consensus. The first contribution of this research is to identify the values of parameters dI and k, such that the transition between fragmented opinions and homogeneity takes place. Then, we look at this transition from two perspectives: first by studying the group size distribution and second by analysing the communication network that is formed by the interactions that take place during the simulation. The emerging networks are classified by statistical means and we find that non-trivial social structures emerge from simple rules for individual communication.

The rise of populism and the reconfiguration of the German political space

The paper explores the notion of a reconfiguration of political space in the context of the rise of populism and its effects on the political system. We focus on Germany and the appearance of the new right wing party "Alternative for Germany" (AfD). Many scholars of politics discuss the rise of the new populism in Western Europe and the US with respect to a new political cleavage related to globalization, which is assumed to mainly affect the cultural dimension of the political space. As such, it might replace the older economic cleavage based on class divisions in defining the dominant dimension of political conflict. An explanation along these lines suggests a reconfiguration of the political space in the sense that (1) the main cleavage within the political space changes its direction from the economic axis towards the cultural axis, but (2) also the semantics of the cultural axis itself is changing towards globalization related topics. Using the electoral manifestos from the Manifesto project database, we empirically address this reconfiguration of the political space by comparing political spaces for Germany built using topic modeling with the spaces based on the content analysis of the Manifesto project and the corresponding categories of political goals. We find that both spaces have a similar structure and that the AfD appears on a new dimension. In order to characterize this new dimension we employ a novel technique, inter-issue consistency networks (IICN) that allow to analyze the evolution of the correlations between the political positions on different issues over several elections. We find that the new dimension introduced by the AfD can be related to the split off of a new "cultural right" issue bundle from the previously existing center-right bundle

Validating argument-based opinion dynamics with survey experiments

The empirical validation of models remains one of the most important challenges in opinion dynamics. In this contribution, we report on recent developments on combining data from survey experiments with computational models of opinion formation. We extend previous work on the empirical assessment of an argument-based model for opinion dynamics in which biased processing is the principle mechanism. While previous work (Banisch & Shamon, in press) has focused on calibrating the micro mechanism with experimental data on argument-induced opinion change, this paper concentrates on the macro level using the empirical data gathered in the survey experiment. For this purpose, the argument model is extended by an external source of balanced information which allows to control for the impact of peer influence processes relative to other noisy processes. We show that surveyed opinion distributions are matched with a high level of accuracy in a specific region in the parameter space, indicating an equal impact of social influence and external noise. More importantly, the estimated strength of biased processing given the macro data is compatible with those values that achieve high likelihood at the micro level. The main contribution of the paper is hence to show that the extended argument-based model provides a solid bridge from the micro processes of argument-induced attitude change to macro level opinion distributions. Beyond that, we review the development of argument-based models and present a new method for the automated classification of model outcomes.Comment: Keywords: opinion dynamics, validation, empirical confirmation, survey experiments, parameter estimation, argument communication theory, computational social scienc

Multidimensional analysis of linguistic networks

Network-based approaches play an increasingly important role in the analysis of data even in systems in which a network representation is not immediately apparent. This is particularly true for linguistic networks, which use to be induced from a linguistic data set for which a network perspective is only one out of several options for representation. Here we introduce a multidimensional framework for network construction and analysis with special focus on linguistic networks. Such a framework is used to show that the higher is the abstraction level of network induction, the harder is the interpretation of the topological indicators used in network analysis. Several examples are provided allowing for the comparison of different linguistic networks as well as to networks in other fields of application of network theory. The computation and the intelligibility of some statistical indicators frequently used in linguistic networks are discussed. It suggests that the field of linguistic networks, by applying statistical tools inspired by network studies in other domains, may, in its current state, have only a limited contribution to the development of linguistic theory.info:eu-repo/semantics/publishedVersio

Im Spannungsfeld von Struktur und Bedeutung

Symptome gesellschaftlicher Spannungen wie Polarisierung, Radikalisierung und gruppenbezogene Feindseligkeit sind sowohl mit Blick auf den Inhalt der Auseinandersetzung zu betrachten, als auch mit Blick auf ihre sozial-strukturelle Ausprägung. Entwicklungen im Bereich der automatisierten Sprachverarbeitung (NLP) sowie der Computational Social Science (CSS) haben ein reiches Arsenal an algorithmischen Methoden zur Inhalts- und Netzwerkanalyse hervorgebracht, und dieser Vortrag ist der Versuch, einen theoretischen Rahmen zu entwickeln, in welchem sich das, was von den verschiedenen Methoden sichtbar gemacht wird, einordnen lässt. Sozio-kognitive Systeme, wie hier konzipiert, legen an, dass Akteure mit einer kognitiven Prägung in kommunikativen Kontexten zusammentreffen und sich durch Verhalten im Rahmen der medialen Gegebenheiten aufeinander beziehen. Es wird ein Beispiel erarbeitet, welches Bezug auf aktuelle Entwicklungen in der DGS sowie auf das Thema „Gesellschaft unter Spannung“ nimmt

Markov chain aggregation for agent-based models

Banisch S. Markov chain aggregation for agent-based models. Bielefeld: Universitätsbibliothek Bielefeld; 2014.This thesis introduces a Markov chain approach that allows a rigorous analysis of a class of agent-based models (ABMs). It provides a general framework of aggregation in agent-based and related computational models by making use of Markov chain aggregation and lumpability theory in order to link between the micro and the macro level of observation. The starting point is a microscopic Markov chain description of the dynamical process in complete correspondence with the dynamical behavior of the agent model, which is obtained by considering the set of all possible agent configurations as the state space of a huge Markov chain. This is referred to as micro chain, and an explicit formal representation including microscopic transition rates can be derived for a class of models by using the random mapping representation of a Markov process. The explicit micro formulation enables the application of the theory of Markov chain aggregation -- namely, lumpability -- in order to reduce the state space of the micro chain and relate microscopic descriptions to a macroscopic formulation of interest. Well-known conditions for lumpability make it possible to establish the cases where the macro model is still Markov, and in this case we obtain a complete picture of the dynamics including the transient stage, the most interesting phase in applications. For such a purpose a crucial role is played by the type of probability distribution used to implement the stochastic part of the model which defines the updating rule and governs the dynamics. Namely, if we decide to remain at a Markovian level, then the partition, or equivalently, the collective variables used to build the macro model must be compatible with the symmetries of the probability distribution ω. This underlines the theoretical importance of homogeneous or complete mixing in the analysis of »voter-like« models at use in population genetics, evolutionary game theory and social dynamics. On the other hand, if a favored level of observation is not compatible with the symmetries in ω, a certain amount of memory is introduced by the transition from the micro level to such a macro description, and this is the fingerprint of emergence in ABMs. The resulting divergence from Markovianity can be quantified using information-theoretic measures and the thesis presents a scenario in which these measures can be explicitly computed. Two simple models are used to illustrate these theoretical ideas: the voter model (VM) and an extension of it called contrarian voter model (CVM). Using these examples, the thesis shows that Markov chain theory allows for a rather precise understanding of the model dynamics in case of »simple« population structures where a tractable macro chain can be derived. Constraining the system by interaction networks with a strong local structure leads to the emergence of meta-stable states in the transient of the model. Constraints on the interaction behavior such as bounded confidence or assortative mating lead to the emergence of new absorbing states in the associated macro chain and are related to stable patterns of polarization (stable co-existence of different opinions or species). Constraints and heterogeneities in the microscopic system and complex social interactions are the basic characteristics of ABMs, and the Markov chain approach to link the micro chain to a macro level description (and likewise the failure of a Markovian link) highlights the crucial role played by those ingredients in the generation of complex macroscopic outcomes