The Impact of Interaction and Social Learning on Aggregate Expectations

Bowden, Mark; McDonald, Stuart D.

doi:10.1007/s10614-007-9118-y

Cited by 13 publications

(8 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Rather interestingly, the herding phenomenon was observed in previous studies on social learning, by using quite different models and assumptions, see e.g. [8], [17].…”

Section: Steady-state Analysismentioning

confidence: 77%

“…The marginalized model (8) can be interpreted as an extension to continuous-time and generalization of the so-called Influence model presented in [2]. As a main difference, we assume here that each agent is simultaneously influenced by all its neighbors, whereas in [2] the interaction mechanism is based on the selection of a single randomly chosen influencing agent at each time step.…”

Section: Remarkmentioning

confidence: 99%

“…Another interesting class of stochastic Markovian models are those used in the social learning literature to describe the behaviour of economic agents, including speculative bubbles, herding and consensus, see e.g. [8], [21], [14]. A distinctive feature of this stream of research is the assumption on the existence of a true "state of the world" that the agents try to learn through observations and communication.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Opinion influence and evolution in social networks: A Markovian agents model

2019

View full text Add to dashboard Cite

In this paper, the effect on collective opinions of filtering algorithms managed by social network platforms is modeled and investigated. A stochastic multi-agent model for opinion dynamics is proposed, that accounts for a centralized tuning of the strength of interaction between individuals. The evolution of each individual opinion is described by a Markov chain, whose transition rates are affected by the opinions of the neighbors through influence parameters. The properties of this model are studied in a general setting as well as in interesting special cases. A general result is that the overall model of the social network behaves like a high-dimensional Markov chain, which is viable to Monte Carlo simulation. Under the assumption of identical agents and unbiased influence, it is shown that the influence intensity affects the variance, but not the expectation, of the number of individuals sharing a certain opinion. Moreover, a detailed analysis is carried out for the so-called Peer Assembly, which describes the evolution of binary opinions in a completely connected graph of identical agents. It is shown that the Peer Assembly can be lumped into a birth-death chain that can be given a complete analytical characterization. Both analytical results and simulation experiments are used to highlight the emergence of particular collective behaviours, e.g. consensus and herding, depending on the centralized tuning of the influence parameters.

show abstract

“…Rather interestingly, the herding phenomenon was observed in previous studies on social learning, by using quite different models and assumptions, see e.g. [8], [17].…”

Section: Steady-state Analysismentioning

confidence: 77%

Section: Remarkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Opinion influence and evolution in social networks: A Markovian agents model

2019

View full text Add to dashboard Cite

show abstract

“…In a cascade situation, where all participants make a decision based upon what other participants have chosen, the stateof-the-world has little influence on behavior. Including just a few experts-who base decisions on the state-of-the-world rather than on the behavior of other agents-can lead to changes in the system when exogenous events occur (Bowden & McDonald, 2008). Similarly, information cascades can be influenced by opinion leaders, who are not necessarily experts, which means that cascade phenomena can sometimes be inhibited by aggregating information so that details-such as who voted for a particular proposition in a meeting-are hidden (Arya, Glover, & Mittendorf, 2006).…”

Section: Information Cascades In Economic Theorymentioning

confidence: 99%

Resonance within the Client-to-Client System: Criticality, Cascades, and Tipping Points

Gill

2008

InformingSciJ

View full text Add to dashboard Cite

Resonance refers to the ability of a rigorous and relevant message to inform and impact a client. In multi-client systems, a critical part of achieving resonance is client-to-client informing. The paper examines this process, developing three alternative models. The Criticality model depends upon the message's ability to motivate clients to resend it; it is most applicable to simple sticky messages, such as rumors and urban myths. The Information Cascade model is client-motivated and depends heavily on the recipient's perception of what other clients are doing; it is most applicable to choice situations where decisions are visible and informing subsequent to a choice is self-regulated by each client. The Tipping Point model is based upon classical diffusion models with heterogeneous individuals (mavens, persuaders, and connectors) introduced into the community; it is most applicable to complex informing situations where continued sender-client involvement is useful throughout the informing process. The behavior of each of these models is studied and spreadsheet-based simulations are also presented. The conclusions characterize each model according to its domain of applicability and also consider how the emerging filed of network theory is impacting our understanding of client-to-client processes.

show abstract

“…These probabilistic approaches by and large aim for positive models of expectations formation, but yield mixed results so far. Bowden and McDonald (2008) study the diffusion of information in various network structures and find a trade-off between volatility in aggregate expectations and the speed at which agents learn the correct state of the world. Secondly, they argue that certain network structures can lead to information cascades.…”

Section: Introductionmentioning

confidence: 99%

Identification of Interaction Effects in Survey Expectations: A Cautionary Note

Alfarano¹,

Milaković²

2012

Studies in Nonlinear Dynamics &Amp; Econometrics

View full text Add to dashboard Cite

A growing body of literature reports evidence of social interaction effects in survey expectations. In this note, we argue that evidence in favor of social interaction effects should be treated with caution, or could even be spurious. Utilizing a parsimonious stochastic model of expectation formation and dynamics, we show that the existing sample sizes of survey expectations are about two orders of magnitude too small to reasonably distinguish between noise and interaction effects. Moreover, we argue that the problem is compounded by the fact that highly correlated responses among agents might not be caused by interaction effects at all, but instead by model-consistent beliefs. Ultimately, these results suggest that existing survey data cannot facilitate our understanding of the process of expectations formation.

show abstract

The Impact of Interaction and Social Learning on Aggregate Expectations

Abstract: Social learning, Expectations formation, Network economics, Information contagion, Volatility, D82, D83,

Cited by 13 publications

References 32 publications

Opinion influence and evolution in social networks: A Markovian agents model

Opinion influence and evolution in social networks: A Markovian agents model

Resonance within the Client-to-Client System: Criticality, Cascades, and Tipping Points

Identification of Interaction Effects in Survey Expectations: A Cautionary Note

Contact Info

Product

Resources

About