Quantifying the effects of social influence

Abstract

How do humans respond to indirect social influence when making decisions? We analysed an experiment where subjects had to guess the answer to factual questions, having only aggregated information about the answers of others. While the response of humans to aggregated information is a widely observed phenomenon, it has not been investigated quantitatively, in a controlled setting. We found that the adjustment of individual guesses depends linearly on the distance to the mean of all guesses. This is a remarkable, and yet surprisingly simple regularity. It holds across all questions analysed, even though the correct answers differ by several orders of magnitude. Our finding supports the assumption that individual diversity does not affect the response to indirect social influence. We argue that the nature of the response crucially changes with the level of information aggregation. This insight contributes to the empirical foundation of models for collective decisions under social influence.

Consensus and Disagreement: The Role of Quantized Behaviors in Opinion Dynamics

“…Introduction. A fundamental assumption in opinion dynamics is that one's opinion is attracted by others', as supported by a substantial literature [31,33,15,37]. If we assume that the opinion of each individual i is described by a variable x i ∈ R, we can describe this attraction by a linear law and thus describe the evolution of the opinions by the following set of differential equations:…”

Persuasive software design patterns for social influence

“…The opinions we hold about subjective matters are often the reflection of the opinion held by others, and these are often-directly or indirectly-influenced by cultural norms, mass media and interactions on social networks [39]. Social influence is defined as a change in one's beliefs, behavior or attitudes caused by external pressures that may be real or imagined [25].…”

“…In order to develop the patterns, relevant literature that highlights the characteristics and issues to consider in PSD [18,32,36,45], social influence [16,25,39,55] and software design patterns [14,24,28,56] will be reviewed.…”

A cooperative learning method based on cellular learning automata and its application in optimization problems

“…We use cellular learning automata (CLA) [10,11] which are cellular automata (CA) [12] in which cells are equipped with learning automata (LA) [13,14], to design a parallel multiagent method. Our method benefits from advantages of both LAs that use reinforcement learning techniques, and CA as a tool for parallelism and a basis 30 for modeling the fact that human decisions are influenced by the behavior of others he refers to [15]. The magnitude of the influence depends on various factors such as trust, relationship, expertness, and etc.…”

“…Reinforcement learning techniques, that are involved in our method, are inspired by behaviorist psychology, in which agents try to figure out how to act efficiently in an environment by interaction only. With each action an agent performs, the environment sends a reinforcement signal 15 to the agent. Reinforcement signals are the only information the agent receives about its acting, and reinforcement techniques are about learning optimal acting in an environment using the reinforcement signals.…”