The Habit Graph

The Majority Illusion in Social Networks

Social behaviors are often contagious, spreading through a population as individuals imitate the decisions and choices of others. A variety of global phenomena, from innovation adoption to the emergence of social norms and political movements, arise as a result of people following a simple local rule, such as copy what others are doing. However, individuals often lack global knowledge of the behaviors of others and must estimate them from the observations of their friends' behaviors. In some cases, the structure of the underlying social network can dramatically skew an individual's local observations, making a behavior appear far more common locally than it is globally. We trace the origins of this phenomenon, which we call "the majority illusion," to the friendship paradox in social networks. As a result of this paradox, a behavior that is globally rare may be systematically overrepresented in the local neighborhoods of many people, i.e., among their friends. Thus, the "majority illusion" may facilitate the spread of social contagions in networks and also explain why systematic biases in social perceptions, for example, of risky behavior, arise. Using synthetic and real-world networks, we explore how the "majority illusion" depends on network structure and develop a statistical model to calculate its magnitude in a network.

Want to Create (and Maintain) a New Habit? Science Says Harness the Power of ‘Temptation Bundling’

Want to create a new habit? Temptation bundling provides the incentive and the motivation.

Reflective and Impulsive Determinants of Social Behavior - Fritz Strack, Roland Deutsch, 2004

This article describes a 2-systems model that explains social behavior as a joint function of reflective and impulsive processes. In particular, it is assumed t...

The "Majority Illusion" in Social Networks

Individual’s decisions, from what product to buy to whether to engage in risky behavior, often depend on the choices, behaviors, or states of other people. People, however, rarely have global knowledge of the states of others, but must estimate them from the local observations of their social contacts. Network structure can significantly distort individual’s local observations. Under some conditions, a state that is globally rare in a network may be dramatically over-represented in the local neighborhoods of many individuals. This effect, which we call the “majority illusion,” leads individuals to systematically overestimate the prevalence of that state, which may accelerate the spread of social contagions. We develop a statistical model that quantifies this effect and validate it with measurements in synthetic and real-world networks. We show that the illusion is exacerbated in networks with a heterogeneous degree distribution and disassortative structure.

The "Majority Illusion" in Social Networks

Individual’s decisions, from what product to buy to whether to engage in risky behavior, often depend on the choices, behaviors, or states of other people. People, however, rarely have global knowledge of the states of others, but must estimate them from the local observations of their social contacts. Network structure can significantly distort individual’s local observations. Under some conditions, a state that is globally rare in a network may be dramatically over-represented in the local neighborhoods of many individuals. This effect, which we call the “majority illusion,” leads individuals to systematically overestimate the prevalence of that state, which may accelerate the spread of social contagions. We develop a statistical model that quantifies this effect and validate it with measurements in synthetic and real-world networks. We show that the illusion is exacerbated in networks with a heterogeneous degree distribution and disassortative structure.

A Network Approach to Investigate the Dynamics of Individual and Collective Beliefs: Advances and Applications of the BENDING Model

Changing entrenched beliefs to alter people’s behavior and increase societal welfare has been at the forefront of behavioral-science research, but with limited success. Here, we propose a new framework of characterizing beliefs as a multidimensional system of interdependent mental representations across three cognitive structures (e.g., beliefs, evidence, and perceived norms) that are dynamically influenced by complex informational landscapes: the BENDING (Beliefs, Evidence, Norms, Dynamic Information Networked Graphs) model. This account of individual and collective beliefs helps explain beliefs’ resilience to interventions and suggests that a promising avenue for increasing the effectiveness of misinformation-reduction efforts might involve graph-based representations of communities’ belief systems. This framework also opens new avenues for future research with meaningful implications for some of the most critical challenges facing modern society, from the climate crisis to pandemic preparedness.

10 Small Habits That Have A Huge Return On Life - Darius Foroux

A lot has been written about forming habits. How long does it take? How do you break habits? How do we adopt new one? Focus on WHAT.

Resistance Is Futile. To Change Habits, Try Replacement Instead. (Published 2018)

An inconvenient bit of neuroscience: The more you try not to think about a bad habit, the harder it gets to resist. So think about something else.

Your Life is Driven by Network Effects

Revealing the Unseen Forces That Guide Your Life   What city you live in. Who you date or marry. Which job you choose. What clothes you wear. We all think we make these choices ourselves. It certainly feels like we’re in full control. But it turns out that our choices — both in our startups and in … Continue reading "Your Life is Driven by Network Effects"

Networked Cooperative Ecosystems

Research TeamAndi Argast (Researcher), Nick Houde (Researcher), Dana Salama, (Researcher & Designer), Samuel Young (Network Mapping), Rithikha Rajamohan (Research Lead).

Retraction Note: High replicability of newly discovered social-behavioural findings is achievable

Nature Human Behaviour - Retraction Note: High replicability of newly discovered social-behavioural findings is achievable

HOWTO: Change your behavior

So, if you desire different behavior, why doesn’t your behavior change as instantly as your desire to change it?

Signaling as a Service

01 Intro One of the best books I have read in the last few years is The Elephant in the Brain by Robin Hanson and Kevin Simler. The book makes two main arguments: a) Most of our everyday actions can be traced back to some form of signaling or status seeking b) Our brains deliberately hi

Bayesian Inference of Social Norms as Shared Constraints on Behavior

People act upon their desires, but often, also act in adherence to implicit social norms. How do people infer these unstated social norms from others' behavior, especially in novel social contexts? We propose that laypeople have intuitive theories of social norms as behavioral constraints shared across different agents in the same social context. We formalize inference of norms using a Bayesian Theory of Mind approach, and show that this computational approach provides excellent predictions of how people infer norms in two scenarios. Our results suggest that people separate the influence of norms and individual desires on others' actions, and have implications for modelling generalizations of hidden causes of behavior.

Social Networking Utopia — Matt

In discussing the network topology of social networks — whether centralized like Facebook or federated like ActivityPub — I think the ult...