Collective dynamics of belief evolution under cognitive coherence and social conformity

Human history has been marked by social instability and conflict, often driven by the irreconcilability of opposing sets of beliefs, ideologies, and religious dogmas. The dynamics of belief systems has been studied mainly from two distinct perspectives, namely how cognitive biases lead to individual belief rigidity and how social influence leads to social conformity. Here we propose a unifying framework that connects cognitive and social forces together in order to study the dynamics of societal belief evolution. Each individual is endowed with a network of interacting beliefs that evolves through interaction with other individuals in a social network. The adoption of beliefs is affected by both internal coherence and social conformity. Our framework explains how social instabilities can arise in otherwise homogeneous populations, how small numbers of zealots with highly coherent beliefs can overturn societal consensus, and how belief rigidity protects fringe groups and cults against invasion from mainstream beliefs, allowing them to persist and even thrive in larger societies. Our results suggest that strong consensus may be insufficient to guarantee social stability, that the cognitive coherence of belief-systems is vital in determining their ability to spread, and that coherent belief-systems may pose a serious problem for resolving social polarization, due to their ability to prevent consensus even under high levels of social exposure. We therefore argue that the inclusion of cognitive factors into a social model is crucial in providing a more complete picture of collective human dynamics

Dynamics of organizational culture: Individual beliefs vs. social conformity

The complex nature of organizational culture challenges our ability to infers its underlying dynamics from observational studies. Recent computational studies have adopted a distinct different view, where plausible mechanisms are proposed to describe a wide range of social phenomena, including the onset and evolution of organizational culture. In this spirit, this work introduces an empirically-grounded, agent-based model which relaxes a set of assumptions that describes past work - (a) omittance of an individual's strive for achieving cognitive coherence, (b) limited integration of important contextual factors - by utilizing networks of beliefs and incorporating social rank into the dynamics. As a result, we illustrate that: (i) an organization may appear to be increasingly coherent in terms of organizational culture, yet be composed of individuals with reduced levels of coherence, (ii) the components of social conformity - peer-pressure and social rank - are influential at different aggregation levels.Comment: 20 pages, 8 figure

Opinion fluctuations and disagreement in social networks

We study a tractable opinion dynamics model that generates long-run disagreements and persistent opinion fluctuations. Our model involves an inhomogeneous stochastic gossip process 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. When the society contains stubborn agents with different opinions, the belief dynamics never lead to a consensus (among the regular agents). Instead, beliefs in the society fail to converge almost surely, the belief profile keeps on fluctuating in an ergodic fashion, and it converges in law to a non-degenerate random vector. The structure of the network and the location of the stubborn agents within it shape the opinion dynamics. The expected belief vector evolves according to an ordinary differential equation coinciding with the Kolmogorov backward equation of a continuous-time Markov chain with absorbing states corresponding to the stubborn agents and converges to a harmonic vector, with every regular agent's value being the weighted average of its neighbors' values, and boundary conditions corresponding to the stubborn agents'. Expected cross-products of the agents' beliefs allow for a similar characterization in terms of coupled Markov chains on the network. We prove that, in large-scale societies which are highly fluid, meaning that the product of the mixing time of the Markov chain on the graph describing the social network and the relative size of the linkages to stubborn agents vanishes as the population size grows large, a condition of \emph{homogeneous influence} emerges, whereby the stationary beliefs' marginal distributions of most of the regular agents have approximately equal first and second moments.Comment: 33 pages, accepted for publication in Mathematics of Operation Researc

Dynamics of Beliefs and Learning Under aL Processes - The Heterogeneous Case

This paper studies a class of models in which agents' expectations influence the actual dynamics while the expectations themselves are the outcome of some recursive processes with bounded memory. Under the assumptions of heterogeneous expectations (or beliefs) and that the agents update their expectations by recursive L- and general aL-processes, the dynamics of the resulting expectations and learning schemes are analyzed. It is shown that the dynamics of the system, including stability, instability and bifurcation, are affected differently by the recursive processes. The cobweb model with a simple heterogeneous expectation scheme is employed as an example to illustrate the stability results, the various types of bifurcations and the routes to complicated price dynamics. In particular, the double edged effect of heterogeneity on the dynamics of the model is demonstrated.heterogeneous beliefs; recursive L-process; general aL-process; stability; instability; bifucation; cobweb model

On the Role of Memory in an Asset Pricing Model with Heterogeneous Beliefs

The paper discusses the role of memory in an asset pricing model with heterogeneous beliefs. In particular, we were interested in how memory in the fitness measure affects the stability of evolutionary adaptive systems and the survival of technical trading. In order to obtain an insight into this matter, two cases were analyzed: a two-type case of fundamentalists versus contrarians and a three-type case of fundamentalists versus opposite biases. It has been established that increasing memory strength has a stabilizing effect on dynamics, though it is not able to eliminate speculative traders’ short-run profit-seeking behaviour from the market. Furthermore, opposite biases do not seem to lead to chaotic dynamics, even when there are no costs for fundamentalists. Apparently some (strong) trend extrapolator beliefs are needed in order to trigger chaotic asset price fluctuations.asset pricing, biased beliefs, contrarians, fitness measure, fundamentalists, heterogeneous beliefs, memory strength, stability

Deliberation and pragmatic belief

To what extent do our beliefs, and how strongly we hold them, depend upon how they matter to us, on what we take to be at stake on them? The idea that beliefs are sometimes stake-sensitive (Armendt 2008, 2013) is further explored here, with a focus on whether beliefs may be stake-sensitive and rational. In contexts of extended deliberation about what to do, beliefs and assessments of options interact. In some deliberations, a belief about what you will do may rationally influence your estimate of the value of doing it; deliberation dynamics provides a framework for modeling such interactions. A distinction is drawn between sensitivity to the magnitude of the stakes, and sensitivity to the shape of the stakes. Contexts of extended deliberation are settings in which some beliefs that p rationally depend on the shape of the stakes on p. The dependence is either rational stake-sensitivity or an outcome of rational learning; empirical evidence concerning contexts of deliberation may lead us to model rational beliefs in one way or the other