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

Design Example: Analyzing Capacity Contours for Flood Risk Assessment01:17

Design Example: Analyzing Capacity Contours for Flood Risk Assessment

43
Flood risk assessment involves careful planning and analysis to ensure the safety of communities near water retention structures. Capacity contours are a vital tool in this process, as they illustrate the potential spread of water at specific levels in a given area. In the context of building a bund across a small valley, these contours play a critical role in evaluating the safety of nearby residential areas.In this example, the bund is intended to store stormwater in the valley. The engineers...
43
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

123
Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
123
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

48
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...
48
Decision Making: P-value Method01:09

Decision Making: P-value Method

5.3K
The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can...
5.3K
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

36
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...
36
Framing Effects03:26

Framing Effects

7.4K
Information is everywhere and its presentation—such as how and when items are presented—can impact our perceptions and decisions surrounding the info. This broad concept umbrellas framing effects—influences that occur due to the way information is framed in its appearance, whether it’s purely the order or the specific wording of a message. Let’s take a look at numerous ways in which two versions of something can objectively say the same thing, yet we respond in...
7.4K

You might also read

Related Articles

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

Sort by
Same author

Can Large Language Models Reason Strategically? Evidence From Attacker-Defender Signaling Games.

Risk analysis : an official publication of the Society for Risk Analysis·2026
Same author

A Systematic Review of Controlling Rumors in Online Social Networks: Insights From Epidemiological Models.

Risk analysis : an official publication of the Society for Risk Analysis·2026
Same author

Game-Theoretic Optimization on School Safety: Resource Allocation Against Strategic Attacks.

Risk analysis : an official publication of the Society for Risk Analysis·2025
Same author

Behavioral validation for a game-theoretic model of attacker strategic decisions, signaling, and deterrence in multi-layer security for soft targets.

Risk analysis : an official publication of the Society for Risk Analysis·2025
Same author

A review of optimization and decision models of prescribed burning for wildfire management.

Risk analysis : an official publication of the Society for Risk Analysis·2024
Same author

An integrated approach to analyze equitable access to food stores under disasters from human mobility patterns.

Risk analysis : an official publication of the Society for Risk Analysis·2024

Related Experiment Video

Updated: Jun 21, 2025

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.0K

Modeling defensive resource allocation in multilayered systems under probabilistic and strategic risks.

Zhiyuan Wei1, Jun Zhuang1

  • 1Department of Industrial and Systems Engineering, University at Buffalo, Buffalo, New York, USA.

Risk Analysis : an Official Publication of the Society for Risk Analysis
|July 11, 2024
PubMed
Summary
This summary is machine-generated.

Security systems benefit from layered defense strategies. This study introduces models for allocating security resources in multilayered systems, optimizing investments against probabilistic and strategic risks for enhanced overall system security.

Keywords:
decision analysisdefensive resource allocationmultilayered defenseprobabilistic and strategic risksrisk analysis

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

12.1K
New Variations for Strategy Set-shifting in the Rat
09:45

New Variations for Strategy Set-shifting in the Rat

Published on: January 23, 2017

8.2K

Related Experiment Videos

Last Updated: Jun 21, 2025

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.0K
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

12.1K
New Variations for Strategy Set-shifting in the Rat
09:45

New Variations for Strategy Set-shifting in the Rat

Published on: January 23, 2017

8.2K

Area of Science:

  • Operations Research
  • Security Science
  • Risk Management

Background:

  • Traditional security models often treat systems as monolithic, overlooking the complexities of layered architectures.
  • Real-world systems, like schools, possess interconnected layers designed to mitigate single points of failure.
  • The need for advanced defensive resource allocation strategies in multilayered security systems is critical.

Purpose of the Study:

  • To develop and analyze novel resource allocation models for multilayered security systems.
  • To account for both probabilistic and strategic risks in defensive investments.
  • To provide insights into optimal security strategies for layered environments.

Main Methods:

  • Development of two new resource allocation models tailored for multilayered systems.
  • Analytical solutions derived for the proposed models.
  • Empirical validation using real-world data from school shootings.

Main Results:

  • In probabilistic risk scenarios, optimal strategies favor increased investment in outer layers over inner layers.
  • For strategic risks, resources are optimally distributed across layers to equalize attacker incentives.
  • Sensitivity analysis reveals key factors influencing optimal security investment strategies.

Conclusions:

  • The study offers significant insights into optimizing security resource allocation within layered systems.
  • Findings demonstrate distinct optimal investment approaches for probabilistic versus strategic risks.
  • The research enhances the understanding of multilayered security system defense, improving overall system resilience.