Jove
Visualize
Contact Us
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 Concept Videos

Decision Making: Traditional Method01:14

Decision Making: Traditional Method

4.9K
The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
4.9K
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

157
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...
157
Decision Making01:20

Decision Making

467
Decision-making is a fundamental cognitive process that involves evaluating alternatives and selecting among them. This process can range from simple choices, such as deciding what to wear, to complex decisions, like choosing a major in college or a career path. The complexity of the decision often dictates the approach we use, which can be broadly categorized into two types: automatic and controlled decision-making.
Automatic decision-making is fast, intuitive, and relies on gut feelings...
467
Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

259
Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
259
Reason and Intuition01:37

Reason and Intuition

7.2K
The human brain processes information for decision-making using one of two routes: an intuitive system and a rational system (Epstein, 1994; popularized by Kahneman, 2011 as System 1 and System 2, respectively). The intuitive system is quick, impulsive, and operates with minimal effort, relying on emotions or habits to provide cues for what to do next, while the rational system is logical, analytical, deliberate, and methodical. Research in neuropsychology suggests that the...
7.2K
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

181
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...
181

You might also read

Related Articles

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

Sort by
Same author

Quantum Superpositions of Conscious States in a Minimal Integrated Information Model.

Entropy (Basel, Switzerland)·2026
Same author

Reply to Rourk, C. Comment on "Albantakis et al. Computing the Integrated Information of a Quantum Mechanism. <i>Entropy</i> 2023, <i>25</i>, 449".

Entropy (Basel, Switzerland)·2023
Same author

Computing the Integrated Information of a Quantum Mechanism.

Entropy (Basel, Switzerland)·2023
See all related articles

Related Experiment Video

Updated: Nov 27, 2025

The Adventures of Fundi Intervention Based on the Cognitive and Emotional Processing in Attention Deficit Hyperactive Disorder Patients
05:48

The Adventures of Fundi Intervention Based on the Cognitive and Emotional Processing in Attention Deficit Hyperactive Disorder Patients

Published on: June 12, 2020

6.1K

A Formal Model for Adaptive Free Choice in Complex Systems.

Ian Durham1

  • 1Department of Physics, Saint Anselm College, Manchester, NH 03102, USA.

Entropy (Basel, Switzerland)
|December 8, 2020
PubMed
Summary

This study introduces a formal model for free will in complex systems, defining free choice as a process. System free will emerges from the aggregate freedom of these individual choice processes.

Area of Science:

  • Complex Systems Science
  • Philosophy of Mind
  • Computational Neuroscience

Background:

  • Traditional models of free will often focus on internal system properties.
  • A gap exists in understanding free will from a behavioral and process-oriented perspective.

Purpose of the Study:

  • To develop a formal, process-based model of free will for complex systems.
  • To quantify the 'freedom' of individual choices and aggregate this to system-level free will.
  • To offer a behavioral perspective on free will, focusing on choice externalities.

Main Methods:

  • Development of a formal model based on process ontology.
  • Introduction of a quantitative measure for the 'freedom' of a singular choice.
  • Modeling free will as an emergent property from aggregate choice process freedom.
Keywords:
agencycausal emergencefree will

More Related Videos

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods
13:04

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods

Published on: September 19, 2012

12.3K
A System for Tracking the Dynamics of Social Preference Behavior in Small Rodents
08:38

A System for Tracking the Dynamics of Social Preference Behavior in Small Rodents

Published on: November 21, 2019

7.9K

Related Experiment Videos

Last Updated: Nov 27, 2025

The Adventures of Fundi Intervention Based on the Cognitive and Emotional Processing in Attention Deficit Hyperactive Disorder Patients
05:48

The Adventures of Fundi Intervention Based on the Cognitive and Emotional Processing in Attention Deficit Hyperactive Disorder Patients

Published on: June 12, 2020

6.1K
Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods
13:04

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods

Published on: September 19, 2012

12.3K
A System for Tracking the Dynamics of Social Preference Behavior in Small Rodents
08:38

A System for Tracking the Dynamics of Social Preference Behavior in Small Rodents

Published on: November 21, 2019

7.9K

Main Results:

  • A formal measure for the freedom of singular choices has been introduced.
  • System free will is defined as emergent from the aggregate freedom of choice processes.
  • The model focuses on the externalities of choice processes, not internal system properties.

Conclusions:

  • The proposed model offers a novel, process-based approach to understanding free will in complex systems.
  • This behavioral perspective does not necessarily conflict with internalist models of free will.
  • The model quantifies free will through emergent properties and adaptive selection of choice processes.