Jove
Visualize
Contact Us

Related Concept Videos

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

438
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
438
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

359
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...
359
Automatic Processing and Automatic Social Behavior01:28

Automatic Processing and Automatic Social Behavior

374
Automatic processing refers to the cognitive operations that occur without conscious intent or awareness, playing a fundamental role in shaping social cognition and behavior. These processes enable individuals to navigate complex social environments efficiently by relying on mental shortcuts and pre-existing knowledge structures known as schemas. One of the most influential mechanisms underlying automatic processing is priming, which subtly activates mental representations through exposure to...
374
Impact of Individuals on Individuals01:30

Impact of Individuals on Individuals

514
Human behavior is intricately shaped by social influences that arise from interactions with others in diverse contexts. These influences not only mold beliefs and attitudes but also drive the regulation of behaviors through both direct communication and observational learning. The study of these processes falls within the domain of social psychology, which seeks to understand how individuals are affected by and affect those around them.Mechanisms of Social InfluenceDirect social influence...
514
Scientific Nature of Social Psychology01:30

Scientific Nature of Social Psychology

762
Social psychology is a scientific discipline dedicated to understanding how individuals think, feel, and behave in social contexts. Unlike common sense, which relies on anecdotal experiences and intuition, social psychology employs systematic research and empirical methods to ensure objectivity and reliability. This distinction is fundamental in distinguishing scientifically supported findings from mere speculation.Four fundamental scientific values guide a structured approach to research in...
762
Defining Social Psychology01:09

Defining Social Psychology

737
Social psychology investigates how the presence and actions of others influence individual behavior, cognition, and emotion. Examining the social environment's impact provides a scientific framework for understanding how individuals perceive others and are, in turn, influenced by them. This field seeks to uncover the underlying principles guiding social interactions, exploring phenomena such as conformity, obedience, and prosocial behavior.Core Themes in Social PsychologyOne central focus of...
737

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Agent Based Modelling for Simulating the Interregional Patient Mobility in Italy.

Studies in health technology and informatics·2023
Same author

Evidence from a long-term experiment that collective risks change social norms and promote cooperation.

Nature communications·2021
Same author

Reputation or peer review? The role of outliers.

Scientometrics·2018
Same author

Counter-Punishment, Communication, and Cooperation among Partners.

Frontiers in behavioral neuroscience·2016
Same author

Mechanism change in a simulation of peer review: from junk support to elitism.

Scientometrics·2014
Same author

Punish and voice: punishment enhances cooperation when combined with norm-signalling.

PloS one·2013
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Apr 26, 2026

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

10.9K

On agent-based modeling and computational social science.

Rosaria Conte1, Mario Paolucci1

  • 1Laboratory of Agent Based Simulation, Institute of Cognitive Science and Technologies, CNR Rome, Italy.

Frontiers in Psychology
|July 30, 2014
PubMed
Summary
This summary is machine-generated.

Agent-based modeling (ABM) can better explain phenomena by utilizing generative theories and rich cognitive models. Computational Social Science (CSS) needs an interdisciplinary approach, integrating ABM with quantitative methods for a robust new program.

Keywords:
agent-based modelingagent-based simulationcomputational social scienceinterdisciplinaritymodel buildingmulti-realizability

More Related Videos

Measuring Neural and Behavioral Activity During Ongoing Computerized Social Interactions: An Examination of Event-Related Brain Potentials
09:40

Measuring Neural and Behavioral Activity During Ongoing Computerized Social Interactions: An Examination of Event-Related Brain Potentials

Published on: November 15, 2014

13.4K
Decoding Natural Behavior from Neuroethological Embedding
08:00

Decoding Natural Behavior from Neuroethological Embedding

Published on: October 3, 2025

885

Related Experiment Videos

Last Updated: Apr 26, 2026

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

10.9K
Measuring Neural and Behavioral Activity During Ongoing Computerized Social Interactions: An Examination of Event-Related Brain Potentials
09:40

Measuring Neural and Behavioral Activity During Ongoing Computerized Social Interactions: An Examination of Event-Related Brain Potentials

Published on: November 15, 2014

13.4K
Decoding Natural Behavior from Neuroethological Embedding
08:00

Decoding Natural Behavior from Neuroethological Embedding

Published on: October 3, 2025

885

Area of Science:

  • Computational Social Science
  • Agent-Based Modeling

Background:

  • Agent-based modeling (ABM) offers strengths in explaining phenomena through generative theories.
  • However, ABM's potential has been limited by an underutilization of its generative power and a preference for simpler models over rich cognitive ones.

Purpose of the Study:

  • To analyze the strengths and weaknesses of agent-based modeling (ABM).
  • To explore the resurgence of Computational Social Science (CSS) and its variants.
  • To propose an interdisciplinary approach for CSS.

Main Methods:

  • Review and analysis of agent-based modeling (ABM) principles.
  • Identification and description of different Computational Social Science (CSS) variants.
  • Conceptualization of an integrated interdisciplinary approach for CSS.

Main Results:

  • The generative capacity of ABM is underexploited, often overshadowed by the demand for simplistic models.
  • Computational Social Science (CSS) is experiencing renewed interest, with distinct deductive, generative, and complex approaches identified.
  • A significant gap exists between purely generative ABM and quantitative approaches in CSS.

Conclusions:

  • Agent-based modeling (ABM) requires deeper integration of generative theories and complex cognitive models.
  • Computational Social Science (CSS) needs a unified, interdisciplinary framework.
  • Reconciling ABM's generative approach with quantitative methods is crucial for advancing CSS.