Gossip Model Research Articles

Gossiping for a Reason – Revised Gossip Functions Questionnaire (RGFQ)

Abstract: Introduction: According to one prominent theoretical model of gossip, we gossip for four key reasons: to keep track of our social networks, to strengthen bonds with others, to manage our reputation, and to entertain ourselves ( Foster, 2004 ). Foster (2004) has also provided the Gossip Functions Questionnaire (GFQ) reflecting these four theoretical functions. Yet, this model has not been empirically derived. Method and Results: Using a large nationally representative sample we show that this survey captures the two main functions of gossip: (1) Normative Function, and (2) Relationship Function. Finally, we modified the GFQ to capture the two main functions of gossip: the 11-item Revised GFQ. Current findings open many avenues of research for social, organizational, and developmental psychologists who are interested in how and when gossip comes about, and how it affects individuals, groups, and organizations.

A mechanistic model of gossip, reputations, and cooperation

Social reputations facilitate cooperation: those who help others gain a good reputation, making them more likely to receive help themselves. But when people hold private views of one another, this cycle of indirect reciprocity breaks down, as disagreements lead to the perception of unjustified behavior that ultimately undermines cooperation. Theoretical studies often assume population-wide agreement about reputations, invoking rapid gossip as an endogenous mechanism for reaching consensus. However, the theory of indirect reciprocity lacks a mechanistic description of how gossip actually generates consensus. Here, we develop a mechanistic model of gossip-based indirect reciprocity that incorporates two alternative forms of gossip: exchanging information with randomly selected peers or consulting a single gossip source. We show that these two forms of gossip are mathematically equivalent under an appropriate transformation of parameters. We derive an analytical expression for the minimum amount of gossip required to reach sufficient consensus and stabilize cooperation. We analyze how the amount of gossip necessary for cooperation depends on the benefits and costs of cooperation, the assessment rule (social norm), and errors in reputation assessment, strategy execution, and gossip transmission. Finally, we show that biased gossip can either facilitate or hinder cooperation, depending on the direction and magnitude of the bias. Our results contribute to the growing literature on cooperation facilitated by communication, and they highlight the need to study strategic interactions coupled with the spread of social information.

PEPPER

Recommender systems are proving to be an invaluable tool for extracting user-relevant content helping users in their daily activities (e.g., finding relevant places to visit, content to consume, items to purchase). However, to be effective, these systems need to collect and analyze large volumes of personal data (e.g., location check-ins, movie ratings, click rates .. etc.), which exposes users to numerous privacy threats. In this context, recommender systems based on Federated Learning (FL) appear to be a promising solution for enforcing privacy as they compute accurate recommendations while keeping personal data on the users' devices. However, FL, and therefore FL-based recommender systems, rely on a central server that can experience scalability issues besides being vulnerable to attacks. To remedy this, we propose PEPPER, a decentralized recommender system based on gossip learning principles. In PEPPER, users gossip model updates and aggregate them asynchronously. At the heart of PEPPER reside two key components: a personalized peer-sampling protocol that keeps in the neighborhood of each node, a proportion of nodes that have similar interests to the former and a simple yet effective model aggregation function that builds a model that is better suited to each user. Through experiments on three real datasets implementing two use cases: a location check-in recommendation and a movie recommendation, we demonstrate that our solution converges up to 42% faster than with other decentralized solutions providing up to 9% improvement on average performance metric such as hit ratio and up to 21% improvement on long tail performance compared to decentralized competitors.

Lower bounds for in-network computation of arbitrary functions

In this paper, we provide a family of bounds for the rate at which the functions of many inputs can be computed in-network on general topologies. Going beyond simple symmetric functions where the output is invariant to the permutation of the operands, e.g., average, parity, we describe an algorithm that is analyzed to provide throughput bounds (both lower and upper) for the general functions. In particular, we analyze our algorithm when the function to be computed is given as a binary tree schema. Our lower bounds depend on schema parameters like the number of operands and graph parameters like the second largest eigenvalue of the transition matrix of simple random walk on network graph, the maximum and minimum degree of any node in the network. The lower bounding technique that we have used is based on the network flows and can capture general multi-commodity flow settings. Our proposed algorithm uses the well-known simple random walk on a network as its basic primitive for routing. We show that the lower bound obtained on the rate of computation is tight for the complete network topology, the hypercube and the star topology. We also present an upper bound on the expected latency of any data operand in terms of the height of schema, well-studied random walk parameter like the hitting time, and the relative distance from the critical data rate. For the computation time of symmetric functions on the random geometric graph under the gossip model, our approach achieves an order-optimal \(\widetilde{O}(n)\) time despite enforcing a binary tree schema for function computation. In general, Big-O notation represents an upper bound and \(\widetilde{O}\) hides \(\text {poly}\log n\) factors.

An attributional process model of workplace gossip.

Despite the ubiquity of gossip in the workplace, the management literature offers a limited understanding of its consequences for gossip senders. To understand whether gossiping is beneficial or detrimental for the gossip sender, it is necessary to consider the perspective of gossip recipients and their response to gossip. We develop a typology of gossip that characterizes archetypal patterns of interpreting gossip. We then draw from attribution theory to develop a multilevel process model of workplace gossip that focuses on how the gossip recipient's attributions of a gossip episode shape the gossip recipient's subsequent response and behaviors. In addition to the valence and work-relatedness dimensions of gossip that comprise the typology, we examine credibility and the status of the gossip target as fundamental features of the gossip episode that jointly affect the gossip recipient's attributions. At the episodic level, the process of deciphering the gossip sender's motives influences the subsequent reciprocation of gossip. Depending on the locus of causality attributed to the gossip episode, gossip also contributes to the perceived trustworthiness of the gossip sender and the gossip recipient's cooperation with or social undermining of the gossip sender over time. The proposed model suggests that the potential benefits or social consequences of gossip for the gossip sender depend on the characteristics of the gossip and the context of the gossip episode that serve as inputs to the gossip recipient's attributional process. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Boolean Gossip Networks

This paper proposes and investigates a Boolean gossip model as a simplified but non-trivial probabilistic Boolean network. With positive node interactions, in view of standard theories from Markov chains, we prove that the node states asymptotically converge to an agreement at a binary random variable, whose distribution is characterized for large-scale networks by mean-field approximation. Using combinatorial analysis, we also successfully count the number of communication classes of the positive Boolean network explicitly in terms of the topology of the underlying interaction graph, where remarkably minor variation in local structures can drastically change the number of network communication classes. With general Boolean interaction rules, emergence of absorbing network Boolean dynamics is shown to be determined by the network structure with necessary and sufficient conditions established regarding when the Boolean gossip process defines absorbing Markov chains. Particularly, it is shown that for the majority of the Boolean interaction rules, except for nine out of the total $2^{16}-1$ possible nonempty sets of binary Boolean functions, whether the induced chain is absorbing has nothing to do with the topology of the underlying interaction graph, as long as connectivity is assumed. These results illustrate the possibilities of relating dynamical properties of Boolean networks to graphical properties of the underlying interactions.

The virtues of gossip: Reputational information sharing as prosocial behavior.

Reputation systems promote cooperation and deter antisocial behavior in groups. Little is known, however, about how and why people share reputational information. Here, we seek to establish the existence and dynamics of prosocial gossip, the sharing of negative evaluative information about a target in a way that protects others from antisocial or exploitative behavior. We present a model of prosocial gossip and the results of 4 studies testing the model's claims. Results of Studies 1 through 3 demonstrate that (a) individuals who observe an antisocial act experience negative affect and are compelled to share information about the antisocial actor with a potentially vulnerable person, (b) sharing such information reduces negative affect created by observing the antisocial behavior, and (c) individuals possessing more prosocial orientations are the most motivated to engage in such gossip, even at a personal cost, and exhibit the greatest reduction in negative affect as a result. Study 4 demonstrates that prosocial gossip can effectively deter selfishness and promote cooperation. Taken together these results highlight the roles of prosocial motivations and negative affective reactions to injustice in maintaining reputational information sharing in groups. We conclude by discussing implications for reputational theories of the maintenance of cooperation in human groups.

Scaling limits for continuous opinion dynamics systems

Scaling limits are analyzed for stochastic continuous opinion dynamics systems, also known as gossip models. In such models, agents update their vector-valued opinion to a convex combination (possibly agent- and opinion-dependent) of their current value and that of another observed agent. It is shown that, in the limit of large agent population size, the empirical opinion density concentrates, at an exponential probability rate, around the solution of a probability-measure-valued ordinary differential equation describing the system’s mean-field dynamics. Properties of the associated initial value problem are studied. The asymptotic behavior of the solution is analyzed for bounded-confidence opinion dynamics, and in the presence of an heterogeneous influential environment.

Gossip for social control in natural and artificial societies

In this work we propose a theory of gossip as a means for social control. Exercising social control roughly means to isolate and to punish cheaters. However, punishment is costly and it inevitably implies the problem of second-order cooperation. Moving from a cognitive model of gossip, we report data from ethnographic studies and agent-based simulations to support our claim that gossip reduces the costs of social control without lowering its efficacy.

Opinion Fluctuations and Disagreement in Social Networks

We study a stochastic gossip model of continuous opinion dynamics in a society consisting of two types of agents: regular agents, who update their beliefs according to information that they receive from their social neighbors; and stubborn agents, who never update their opinions and might represent leaders, political parties or media sources attempting to influence the beliefs in the rest of the society. When the society contains stubborn agents with different opinions, opinion dynamics never lead to a consensus (among the regular agents). Instead, beliefs in the society almost surely fail to converge, and the belief of each regular agent converges in law to a non-degenerate random variable. The model thus generates long-run disagreement and continuous opinion fluctuations. The structure of the social network and the location of stubborn agents within it shape opinion dynamics. When the society is “highly fluid”, meaning that the mixing time of the random walk on the graph describing the social network is small relative to (the inverse of) the relative size of the linkages to stubborn agents, the ergodic beliefs of most of the agents concentrate around a certain common value. We also show that under additional conditions, the ergodic beliefs distribution becomes “approximately chaotic”, meaning that the variance of the aggregate belief of the society vanishes in the large population limit while individual opinions still fluctuate significantly.

The age of gossip

Disseminating a piece of information, or updates for a piece of information, has been shown to benefit greatly from simple randomized procedures, sometimes referred to as gossiping, or epidemic algorithms. Similarly, in a network where mobile nodes occasionally receive updated content from a base station, gossiping using opportunistic contacts allows for recent updates to be efficiently maintained, for a large number of nodes. In this case, however, gossiping depends on node mobility. For this reason, we introduce a new gossip model, with mobile nodes moving between different classes that can represent locations or states, which determine gossiping behavior of the nodes. Here we prove that, when the number of mobile nodes becomes large, the age of the latest updates received by mobile nodes approaches a deterministic mean-field regime. More precisely, we show that the occupancy measure of the process constructed, with the ages defined above, converges to a deterministic limit that can be entirely characterized by differential equations. This major simplification allows us to characterize how mobility, source inputs and gossiping influence the age distribution for low and high ages. It also leads to a scalable numerical evaluation of the performance of mobile update systems, which we validate (using a trace of 500 taxicabs) and use to propose infrastructure deployment.

Utilities of gossip across organizational levels : Multilevel selection, free-riders, and teams.

Gossip is a subject that has been studied by researchers from an array of disciplines with various foci and methods. We measured the content of language use by members of a competitive sports team across 18 months, integrating qualitative ethnographic methods with quantitative sampling and analysis. We hypothesized that the use of gossip will vary significantly depending on whether it is used for self-serving or group-serving purposes. Our results support a model of gossip derived from multilevel selection theory that expects gossip to serve group-beneficial rules when rewards are partitioned at the group level on a scale that permits mutual monitoring. We integrate our case study with earlier studies of gossip conducted by anthropologists, psychologists, and management researchers.

Dialogue

Academy of Management ReviewVol. 26, No. 2 ArticlesDialoguePublished Online:1 Apr 2001https://doi.org/10.5465/amr.2001.4378005AboutSectionsView articleView Full TextPDF/EPUB ToolsDownload CitationsAdd to favoritesTrack Citations ShareShare onFacebookTwitterLinkedInRedditEmail View articleAbstractCommentaries are presented pertaining to the article written by Rynes an Trank involving organizational behavior and human resource management teaching in schools, as well as Nancy Kurland and Lisa Pelled's conceptual model of workplace gossip and its effects on the power of employees who initiate it.FiguresReferencesRelatedDetails Vol. 26, No. 2 Permissions Metrics in the past 12 months History Published online 1 April 2001 Published in print 1 April 2001 Information© Academy of Management ReviewKeywordsCRITICISMACADEMY of Management Review (Periodical)PSYCHOLOGYMANAGEMENT scienceMANAGEMENT researchBUSINESS schoolsGOSSIPDownload PDF

Passing the Word: Toward a Model of Gossip and Power in the Workplace

Although gossip is widespread, seldom has it been a topic of management research. Here the researchers build a conceptual model of workplace gossip and its effects on the power of employees who initiate it. After defining and distinguishing among different kinds of workplace gossip, the researchers develop propositions about the effect of that gossip on gossipers' expert, referent, reward, and coercive power. They then suggest how moderators may shape those effects and discuss implications of the model.

Passing the Word: Toward a Model of Gossip and Power in the Workplace

Passing the Word: Toward a Model of Gossip and Power in the Workplace