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Confirmation Biases01:31

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The confirmation bias is the tendency to focus on information that confirms our existing beliefs and ignore information that is inconsistent with our expectations. For example, if you think that your professor is not very nice, you notice all of the instances of rude behavior exhibited by the professor while ignoring the countless pleasant interactions he is involved in on a daily basis. Have you ever fallen prey to the confirmation bias, either as the source or target of such bias?
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Electrocyclic reactions, cycloadditions, and sigmatropic rearrangements are concerted pericyclic reactions that proceed via a cyclic transition state. These reactions are stereospecific and regioselective. The stereochemistry of the products depends on the symmetry characteristics of the interacting orbitals and the reaction conditions. Accordingly, pericyclic reactions are classified as either symmetry-allowed or symmetry-forbidden. Woodward and Hoffmann presented the selection criteria for...
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The actor-observer effect, a cognitive bias closely linked to the fundamental attribution error, refers to the tendency for individuals to attribute their behavior to external, situational factors while explaining others’ behavior in terms of internal, dispositional traits. This asymmetry in attribution significantly influences social perception and judgment.Cognitive Mechanisms Behind the EffectTwo primary psychological mechanisms contribute to the actor-observer effect: differences in...
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The Two-State Receptor Model01:29

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The two-state receptor model explains a drug's interaction with receptors, such as G protein-coupled receptors and ligand-gated ion channels, to induce or inhibit a biological response. When no natural ligands are present, a receptor exists in an equilibrium of inactive (Ri) and active (Ra) conformations. The inactive form does not produce a response, while the active form generates a basal effect known as constitutive activity.
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Drugs administered through various routes can lead to nonlinear elimination, resulting in complex pharmacokinetic behaviors crucial to understanding efficacious drug dosing.
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Multi-input and Multi-variable systems01:22

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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
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Updated: Sep 25, 2025

The HoneyComb Paradigm for Research on Collective Human Behavior
06:48

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Filter bubble effect in the multistate voter model.

Giulio Iannelli1, Giordano De Marzo1, Claudio Castellano1

  • 1Centro Ricerche Enrico Fermi, Piazza del Viminale, 1, I-00184 Rome, Italy.

Chaos (Woodbury, N.Y.)
|April 30, 2022
PubMed
Summary
This summary is machine-generated.

Social media algorithms create filter bubbles by recommending past preferences. This can lead to polarization, preventing consensus among users, especially in large online communities.

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Area of Science:

  • Computational Social Science
  • Network Science
  • Information Science

Background:

  • Social media platforms utilize recommendation algorithms to personalize user content.
  • This personalization can lead to filter bubbles, limiting exposure to diverse viewpoints.
  • Understanding the impact of these algorithms on user opinion dynamics is crucial.

Purpose of the Study:

  • To investigate the impact of personalized information on opinion dynamics within a social network.
  • To model the emergence of consensus versus polarization under personalized recommendation scenarios.

Main Methods:

  • Utilized a multistate voter model to simulate user interactions and opinion formation.
  • Introduced a probability parameter (λ) representing interaction with personalized information.
  • Employed theoretical analysis and numerical simulations to study system behavior.

Main Results:

  • Identified a nontrivial transition point (λc) separating consensus and polarization regimes.
  • Observed that for small λ, consensus is reached, while above λc, persistent opinion clusters emerge.
  • Demonstrated that the polarization threshold vanishes for large system sizes (N), making consensus impossible.

Conclusions:

  • Personalized recommendation algorithms can drive polarization in online social networks.
  • The widespread use of such algorithms may hinder the formation of societal consensus.
  • Further research is needed to address the unintended consequences of personalized content delivery.