Memetics and voting: how nature may make us public spirited

We consider the classic puzzle of why people turn out for elections in substantial numbers even though formal analysis strongly suggests that rational agents would not vote. If one assumes that voters do not make systematic mistakes, the most plausible explanation seems to be that agents receive a warm glow from the act of voting itself. However, this begs the question of why agents feel a warm glow from participating in the electoral process in the first place. We approach this question from a memetic standpoint. More specifically, we consider a model in which social norms, ideas, values, or more generally, “memes”, influence the behavior of groups of agents, and in turn, induce a kind of competition between value systems. We show, for a range of situations, that groups with a more public-spirited social norm have an advantage over groups that are not as public-spirited. We also explore conditions under which the altruistic behavior resulting from public-spiritedness is disadvantageous. The details depend on the costs of voting, the extent to which different types of citizens agree or disagree over the benefits of various public policies, and the relative proportions of various preference types in the population. We conclude that memetic evolution over social norms may be a force that causes individuals to internalize the benefits that their actions confer on others.     

Effect of local information within network layers on the evolution of cooperation in duplex public goods games

Traditional works of public goods game (PGG) are often studied in simplex networks where agents play games through the same type of social interactions. In order to promote cooperation against the defection in PGGs in simplex network environment, many mechanisms have been proposed from different perspectives, such as the volunteering mechanisms, and the punishment and reward approaches. However, due to diverse types of interactions between agents in reality, the study of PGG should also consider the characteristic of multiplexity of networks. Hence, we firstly model the public goods game in the duplex network (for simplification of analysis, the duplex network is considered), in which agents have two types of social interactions, and thus the network is modeled as two network layers. This type of PGG is naturally named as duplex public goods game (D-PGG), in which agents can select one of the network layers to allocate their limited resources. Then for the new game environment (D-PGG), we propose a novel perspective to promote cooperation: degrading the information integrity, i.e., agents get information just from one network layer (local information) rather than from the whole duplex network (global information) in the evolution process. Finally, through theoretical analyses and simulations, we find that if agents imitate based on the local information of the payoff in the evolution, cooperation can be generally promoted; and the extent of promotion depends on both the network structure and the similarity of the network layers.     

Combining evolutionary game theory and network theory to analyze human cooperation patterns

As natural systems continuously evolve, the human cooperation dilemma represents an increasingly more challenging question. Humans cooperate in natural and social systems, but how it happens and what are the mechanisms which rule the emergence of cooperation, represent an open and fascinating issue. In this work, we investigate the evolution of cooperation through the analysis of the evolutionary dynamics of behaviours within the social network, where nodes can choose to cooperate or defect following the classical social dilemmas represented by Prisoner’s Dilemma and Snowdrift games. To this aim, we introduce a sociological concept and statistical estimator, “Critical Mass”, to detect the minimum initial seed of cooperators able to trigger the diffusion process, and the centrality measure to select within the social network. Selecting different spatial configurations of the Critical Mass nodes, we highlight how the emergence of cooperation can be influenced by this spatial choice of the initial core in the network. Moreover, we target to shed light how the concept of homophily, a social shaping factor for which “birds of a feather flock together”, can affect the evolutionary process. Our findings show that homophily allows speeding up the diffusion process and make quicker the convergence towards human cooperation, while centrality measure and thus the Critical Mass selection, play a key role in the evolution showing how the spatial configurations can create some hidden patterns, partially counterbalancing the impact of homophily.     

Structure and emergence in a nested logit model with social and spatial interactions

Suppose you have the possibility to choose to adopt one of a number of different behaviors or to choose to buy one of a number of different products, and suppose your choice is influenced by your individual perception of the average choices made by others. Economists Brock and Durlauf (in Am. Econ. Rev. 92(2):298, 2002; The Economy as an Evolving Complex System III. Oxford University Press, New York, 2006) have derived seminal theoretical results for the equilibrium behavior of the multinomial discrete choice model with social interactions, assuming homogeneous decision-makers, global interactions and laws of large of numbers. The research presented in this paper extends Brock and Durlauf’s model to allow for unobserved preference heterogeneity between choice alternatives by studying the nested logit model. Next, by drawing on the computational possibilities permitted through social simulation of multi-agent systems (MAS), this paper relaxes the assumption of global interactions and considers instead local interactions within several hypothesized social and spatial network structures. Additional heterogeneity is thus hereby induced by the influence on a given decision-maker’s choice by the particular network connections he or she has and the particular perceived percentages, for example, of the agent’s neighbors or socio-economic peers making each choice. Discrete choice estimation results controlling these heterogeneous individual preferences are embedded in a multi-agent based simulation model in order to observe the evolution of choice behavior over time with socio-dynamic feedback due to the network effects. The MAS approach also gives us an additional advantage in the possibility to test size effects, and thus relax the assumption of large numbers, as well as test the effect of different initial conditions. Finally an extra benefit is gained via the MAS approach in that we are not confined to study only the equilibrium behavior, and have the possibility here to observe the time-varying trajectories of the choice behavior. This is important since smaller network sizes are revealed to be associated with higher volatility of the choice behavior in this model, and consequently stochastic cycling between equilibria. Averaged over time, the emergent behavior in such case yields a quite different picture than the theoretical results predicted by Brock and Durlauf. Furthermore being able to observe the emergent behavior allows us to see the subtle role of the unobserved heterogeneity in the nested logit model in breaking the symmetry of the multinomial logit model. We can see the temporal patterns by which theoretically predicted dominant equilibria emerge or not according to different social and spatial network scenarios. With an eye towards application in the context of transportation mode choice, we conclude highlighting limitations of our present study and recommendations for future work.     

How behaviors spread in dynamic social networks

In this paper, we explore how decentralized local interactions of autonomous agents in a network relate to collective behaviors. Earlier work in this area has modeled social networks with fixed agent relations. We instead focus on dynamic social networks in which agents can rationally adjust their neighborhoods based on their individual interests. We propose a new connection evaluation theory, the Highest Weighted Reward (HWR) rule: agents dynamically choose their neighbors in order to maximize their own utilities based on rewards from previous interactions. We prove that, in the two-action pure coordination game, our system would stabilize to a clustering state in which all relationships in the network are rewarded with an optimal payoff. Our experiments verify this theory and also reveal additional interesting patterns in the network.     

Modelling and estimation of social interaction effects in new product diffusion

We present a new product choice model in which individual agents are assumed to interact with each other across spatial hierarchies under random Gaussian laws. A restricted form that allows estimation of the model and inference on the random interaction structure on available data is then derived. This is essentially a random coefficients model which we estimate on repeated cross-sectional data for the adoption of video cassette recorders in Britain. It is argued that the random coefficient approach, based on economic and social-theoretic arguments allows for more accurate inference on the structure of social interactions, and thus may provide a useful way to help alleviate the inferential problem caused by the identification issues stated in the literature. This very same problem has for long been at the core of the debate between alternative economic and marketing theories of new product diffusion.     

Small world network models of the dynamics of HIV infection

It has long been recognised that the structure of social networks plays an important role in the dynamics of disease propagation. The spread of HIV results from a complex network of social interactions and other factors related to culture, sexual behaviour, demography, geography and disease characteristics, as well as the availability, accessibility and delivery of healthcare. The small world phenomenon has recently been used for representing social network interactions. It states that, given some random connections, the degrees of separation between any two individuals within a population can be very small. In this paper we present a discrete event simulation model which uses a variant of the small world network model to represent social interactions and the sexual transmission of HIV within a population. We use the model to demonstrate the importance of the choice of topology and initial distribution of infection, and capture the direct and non-linear relationship between the probability of a casual partnership (small world randomness parameter) and the spread of HIV. Finally, we illustrate the use of our model for the evaluation of interventions such as the promotion of safer sex and introduction of a vaccine.     

Human mate choice is a complex system

From a psychological perspective, human mate choice has been viewed as a problem of identifying the individual cognitive preferences and decisions that explain empirical results such as similarity in attractiveness between mates and the right‐skewed unimodal marriage hazard curves for marriage rates. Agent‐based models provide a powerful theoretical tool for investigating this relationship, but until now have not considered the effects of local neighborhoods or mobility on emergent population dynamics. In failing to do so, they have effectively ruled out the population‐level complexity inherent in human mate choice. Real people live in physical space, and their interactions are constrained by their location in and mobility among physical neighborhoods and social networks. We developed a general model of human mate choice in which agents are localized in space, interact with close neighbors, and tend to range either near or far. At the individual level, our model uses two oft‐used but incompletely understood decision rules: one based on preferences for similar partners, the other for maximally attractive partners. We show that space and mobility can interact nonlinearly with these individual decision rules and nonspatial aspects of the population structure. In particular, local interactions and limited mobility decrease interpair matching and increase mate search time. We also show that it is too easy to fit various model configurations to the scant available data. More data and more specific predictions are required. Human mate choice is a complex system with properties that emerge from space, mobility, and other factors that structure social dynamics. © 2011 Wiley Periodicals, Inc. Complexity, 2011.     

Introduction to the Special Issue

In the model, each person in a large population chooses between two options, such as adhering to or not adhering to a social norm. People observe each others’ choices at random and adjust their adherence probabilities in imitative directions. It is known from earlier work that, under strong restrictions on the imitation, the distribution of adherence probabilities will either evolve upward toward a high adherence equilibrium or downward toward a low adherence equilibrium, depending on initial conditions. The intuition is that imitation leads to uniformity. Here we show that more general forms of imitation allow a much wider variety of outcomes. There can be a sizable number of equilibria and a variety of stability patterns. In mathematical form, the model is an interactive Markov chain.     

Iterative methods for solvingAx=b,GMRES/FOM versus QMR/BiCG

We study the convergence of the GMRES/FOM and QMR/BiCG methods for solving nonsymmetric systems of equationsAx=b. We prove, in exact arithmetic, that any type of residual norm convergence obtained using BiCG can also be obtained using FOM but on a different system of equations. We consider practical comparisons of these procedures when they are applied to the same matrices. We use a unitary invariance shared by both methods, to construct test matrices where we can vary the nonnormality of the test matrix by variations in simplified eigenvector matrices. We used these test problems in two sets of numerical experiments. The first set of experiments was designed to study effects of increasing nonnormality on the convergence of GMRES and QMR. The second set of experiments was designed to track effects of the eigenvalue distribution on the convergence of QMR. In these tests the GMRES residual norms decreased significantly more rapidly than the QMR residual norms but without corresponding decreases in the error norms. Furthermore, as the nonnormality ofA was increased, the GMRES residual norms decreased more rapidly. This led to premature termination of the GMRES procedure on highly nonnormal problems. On the nonnormal test problems the QMR residual norms exhibited less sensitivity to changes in the nonnormality. The convergence of either type of procedure, as measured by the error norms, was delayed by the presence of large or small outliers and affected by the type of eigenvalues, real or complex, in the eigenvalue distribution ofA. For GMRES this effect can be seen only in the error norm plots.In honor of the 70th birthday of Ted RivlinThis work was supported by NSF grant GER-9450081.     

Self‐organization and selection in the emergence of vocabulary

Human language may have started from a consistent set of mappings between meanings and signals. These mappings, referred to as the early vocabulary, are considered to be the results of conventions established among the agents of a population. In this study, we report simulation models for investigating how such conventions can be reached. We propose that convention is essentially the product of self‐organization of the population through interactions among the agents and that cultural selection is another mechanism that speeds up the establishment of convention. Whereas earlier studies emphasize either one or the other of these two mechanisms, our focus is to integrate them into one hybrid model. The combination of these two complementary mechanisms, i.e., self‐organization and cultural selection, provides a plausible explanation for cultural evolution, which progresses with high transmission rate. Furthermore, we observe that as the vocabulary tends to convergence there is a uniform tendency to exhibit a sharp phase transition. © 2002 Wiley Periodicals, Inc.     

Uniformity seminorms on ℓ<Superscript>∞</Superscript> and applications

A key tool in recent advances in understanding arithmetic progressions and other patterns in subsets of the integers is certain norms or seminorms. One example is the norms on ℤ/Nℤ introduced by Gowers in his proof of Szemerédi’s Theorem, used to detect uniformity of subsets of the integers. Another example is the seminorms on bounded functions in a measure preserving system (associated to the averages in Furstenberg’s proof of Szemerédi’s Theorem) defined by the authors. For each integer k ≥ 1, we define seminorms on ℓ^∞(ℤ) analogous to these norms and seminorms. We study the correlation of these norms with certain algebraically defined sequences, which arise from evaluating a continuous function on the homogeneous space of a nilpotent Lie group on a orbit (the nilsequences). Using these seminorms, we define a dual norm that acts as an upper bound for the correlation of a bounded sequence with a nilsequence. We also prove an inverse theorem for the seminorms, showing how a bounded sequence correlates with a nilsequence. As applications, we derive several ergodic theoretic results, including a nilsequence version of the Wiener-Wintner ergodic theorem, a nil version of a corollary to the spectral theorem, and a weighted multiple ergodic convergence theorem.     

Network manipulation algorithm based on inexact alternating minimization

In this paper, we present a network manipulation algorithm based on an alternating minimization scheme from Nesterov (Soft Comput 1–12, 2020). In our context, the alternative process mimics the natural behavior of agents and organizations operating on a network. By selecting starting distributions, the organizations determine the short-term dynamics of the network. While choosing an organization in accordance with their manipulation goals, agents are prone to errors. This rational inattentive behavior leads to discrete choice probabilities. We extend the analysis of our algorithm to the inexact case, where the corresponding subproblems can only be solved with numerical inaccuracies. The parameters reflecting the imperfect behavior of agents and the credibility of organizations, as well as the condition number of the network transition matrix have a significant impact on the convergence of our algorithm. Namely, they turn out not only to improve the rate of convergence, but also to reduce the accumulated errors. From the mathematical perspective, this is due to the induced strong convexity of an appropriate potential function.

     

Collective increase of first impression bias

We propose a simple model of first impression bias (FIB), where agents tend to ignore features which contradict their initial view. We consider a population of agents which are all in contact with a media, communicating randomly chosen features of an object. In some cases, we observe on simulations that FIB is significantly more frequent when the agents interact with each other than when they are only in contact with the media. We design an analytical aggregated model of the global agent‐based model behavior, which helps to explain the higher number of FIB due to the interactions. © 2009 Wiley Periodicals, Inc. Complexity, 2010     

On a Class of Random Variational Inequalities on Random Sets

We study a class of random variational inequalities on random sets and give measurability, existence, and uniqueness results in a Hilbert space setting. In the special case where the random and the deterministic variables are separated, we present a discretization technique based on averaging and truncation, prove a Mosco convergence result for the feasible random set, and establish norm convergence of the approximation procedure.     

Reasoning about norms under uncertainty in dynamic environments

The behaviour of norm-autonomous agents is determined by their goals and the norms that are explicitly represented inside their minds. Thus, they require mechanisms for acquiring and accepting norms, determining when norms are relevant to their case, and making decisions about norm compliance. Up until now the existing proposals on norm-autonomous agents assume that agents interact within a deterministic environment that is certainly perceived. In practise, agents interact by means of sensors and actuators under uncertainty with non-deterministic and dynamic environments. Therefore, the existing proposals are unsuitable or, even, useless to be applied when agents have a physical presence in some real-world environment. In response to this problem we have developed the n-BDI architecture. In this paper, we propose a multi-context graded BDI architecture (called n-BDI) that models norm-autonomous agents able to deal with uncertainty in dynamic environments. The n-BDI architecture has been experimentally evaluated and the results are shown in this paper.     

Meet,discuss, and segregate!

We present a model of opinion dynamics in which agents adjust continuous opinions as a result of random binary encounters whenever their difference in opinion is below a given threshold. High thresholds yield convergence of opinions toward an average opinion, whereas low thresholds result in several opinion clusters. The model is further generalized to network interactions, threshold heterogeneity, adaptive thresholds, and binary strings of opinions. © 2002 Wiley Periodicals, Inc.     

Structural Learning: Attraction and Conformity in Task-Oriented Groups

This study extends previous research that showed how informal social sanctions can backfire when members prefer friendship over enforcement of group norms. We use a type of neural network to model the coordination of informal social control in a small group of adaptive agents confronted with a social dilemma. This model incorporates two mechanisms of social influence, informal sanctions and imitation. Both mechanisms vary with the strength of the social tie between source and target. Previous research focused on the effects of social sanctions. Here, we demonstrate a curvilinear effect of imitation on compliance with prosocial norms. Moderate doses of imitation reduce the coordination complexity of self-organized collective action and help the network achieve satisfactory levels of cooperation. High doses, however, undermine the agent-based learning required to find cooperative solutions. Increasing group size also diminishes compliance due to increased complexity, with larger groups requiring more imitation to overcome the coordination problem.     

Fourier-Based Distances and Berry-Esseen Like Inequalities for Smooth Densities

 This paper is devoted to the rate of convergence problem in the central limit theorem for sums of independent identically distributed random variables with regular probability density function. The method we use depends strictly on Fourier based metrics, and yields Berry-Esseen like bounds for the convergence towards both a normal and a stable law in various Sobolev norms.     

Random Tight Frames

We introduce probabilistic frames to study finite frames whose elements are chosen at random. While finite tight frames generalize orthonormal bases by allowing redundancy, independent, uniformly distributed points on the sphere approximately form a finite unit norm tight frame (FUNTF). In the present paper, we develop probabilistic versions of tight frames and FUNTFs to significantly weaken the requirements on the random choice of points to obtain an approximate finite tight frame. Namely, points can be chosen from any probabilistic tight frame, they do not have to be identically distributed, nor have unit norm. We also observe that classes of random matrices used in compressed sensing are induced by probabilistic tight frames.