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Agonism and Antagonism: Quantification01:14

Agonism and Antagonism: Quantification

556
When drugs are administered, they can elicit either an agonist or antagonist effect on the body. Agonism occurs when a drug activates a specific receptor, triggering a biological response. On the other hand, antagonism happens when a drug binds to the same receptors but blocks their activation, thereby preventing a biological response.
To quantify these effects, researchers use a dose-response curve, which provides valuable information about the potency and efficacy of a drug. Potency refers to...
556
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

85
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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Cognitivism01:17

Cognitivism

1.7K
Cognitive psychology emerged as a significant field in the mid-20th century. It focused on understanding humans' internal mental processes. This approach emphasizes how people perceive, remember, think, and solve problems—elements critical to human cognition.
Previously dominated by behaviorism, which prioritized observable behaviors and largely ignored mental processes, psychology transformed in the 1950s. Cognitive psychologists argue that understanding how we think and process...
1.7K
Observational Learning01:12

Observational Learning

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Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
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Naturalistic Observations02:30

Naturalistic Observations

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If you want to understand how behavior occurs, one of the best ways to gain information is to simply observe the behavior in its natural context. However, people might change their behavior in unexpected ways if they know they are being observed. How do researchers obtain accurate information when people tend to hide their natural behavior? As an example, imagine that your professor asks everyone in your class to raise their hand if they always wash their hands after using the restroom. Chances...
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Attribution Theory00:56

Attribution Theory

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Behavior is a product of both the situation (e.g., cultural influences, social roles, and the presence of bystanders) and of the person (e.g., personality characteristics). Subfields of psychology tend to focus on one influence or behavior over others. Situationism is the view that our behavior and actions are determined by our immediate environment and surroundings. In contrast, dispositionism holds that our behavior is determined by internal factors (Heider, 1958).
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Updated: Sep 8, 2025

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

The HoneyComb Paradigm for Research on Collective Human Behavior

Published on: January 19, 2019

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エージェントベースのモデルで議論を捉える

Leon Assaad1, Rafael Fuchs2, Kirsty Phillips3

  • 1Munich Center for Mathematical Philosophy, LMU, Geschwister-Scholl-Platz 1, 80539 Munich, Bavaria Germany.

Topoi : an international review of philosophy
|August 20, 2025
PubMed
まとめ
この要約は機械生成です。

エージェントベースのモデル (ABM) は,議論を理由,シロジズム,および弁証法として区別することで複雑な議論をモデル化することができます. ベイジアンネットワークを使用するNormANフレームワークは,これらのモデルを比較し,議論の拡散を研究するための新しい方法を提供します.

キーワード:
エージェントベースのモデリング議論ベイジアン認識論弁証法社会知識論

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関連する実験動画

Last Updated: Sep 8, 2025

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科学分野:

  • コンピュータ社会科学
  • 人工知能
  • 認知科学

背景:

  • エージェントベースのモデル (ABM) は,複雑なシステムと新興特性を研究するために一般的です.
  • ABMの信念と意見の動態は オンラインのソーシャルメディアと科学的な議論に 関連しています
  • 現存するABMは,リッチな議論のシナリオを完全に捉えていません.

研究 の 目的:

  • エージェントベースの議論の枠組みを導入する.
  • 議論の3つの概念を区別し分析する. 理由,シロジズム,そして弁証法.
  • 議論モデルを比較し選択するための組織化スキームを提供する.

主な方法:

  • 断言的な内容 (理由),前提と結論の関係 (シロジズム),会話的展開 (弁証法) として議論を区別する.
  • 参照モデルとして,ベイジアンネットワーク上に ABM を構築する NormAN フレームワークを使用します.
  • 議論の各概念をモデル化する複雑性の連続性を分析する.

主要な成果:

  • エージェントベースの議論モデルを比較し,選択するための新しい組織化スキーム.
  • 議論の3つの概念がどのように相互に制約されているかを明確にします.
  • ノルマン・フレームワークは,議論の拡散に馴染みのある議論の側面と弁証法的な影響を捉えていることを示しています.

結論:

  • エージェントベースのモデルは,対話的側面を含む複雑な議論を効果的に表すことができます.
  • NormAN フレームワークは,議論の交換をモデル化するための汎用的なアプローチを提供します.
  • 議論の拡散にどう影響するか 更に研究が進められます