Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Propagation of Uncertainty from Random Error

Propagation of Uncertainty from Random Error

An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...

Propagation of Uncertainty from Systematic Error

Propagation of Uncertainty from Systematic Error

The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...

Observational Learning

Observational Learning

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...

Randomized Experiments

Randomized Experiments

The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...

Expected Value

Expected Value

The expected value is known as the "long-term" average or mean. This means that over the long term of experimenting over and over, you would expect this average. The expected average is represented by the symbol μ. It is calculated as follows:

Prediction Intervals

Prediction Intervals

The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.

You might also read

Related Articles

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

Sort by

Same author

Emergent Resource Exchange and Tolerated Theft Behavior Using Multiagent Reinforcement Learning.

Artificial life·2024

Same author

Quantitative EEG as a Prognostic Tool in Suspected Anti-N-Methyl- d -Aspartate Receptor Antibody Encephalitis.

Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society·2021

Same author

Nanofluid Boiling on Micro/Nano-engineered Surfaces.

Langmuir : the ACS journal of surfaces and colloids·2021

Same author

A 9-month retrospective evaluation of the aetiology and management of patients presenting with encephalitis/meningoencephalitis at a South London hospital.

Epidemiology and infection·2020

Same author

Evolving Complexity in Prediction Games.

Artificial life·2019

Same author

Escalation of Memory Length in Finite Populations.

Artificial life·2019

Search research articles

Related Experiment Video

Updated: Jan 4, 2026

The HoneyComb Paradigm for Research on Collective Human Behavior

The HoneyComb Paradigm for Research on Collective Human Behavior

Published on: January 19, 2019

Evolving Complexity in Cooperative and Competitive Noisy Prediction Games.

Nick Moran¹, Jordan Pollack²

¹Brandeis University, DEMO Lab. nemtiax@gmail.com.

Artificial Life

|November 8, 2019

Summary

Cooperative and competitive interactions drive the evolution of complex strategies in noisy prediction games. A novel metric effectively measures this complexity, distinguishing it from simple genetic expansion.

Keywords:

Coevolution competition complexity cooperation ecosystems

More Related Videos

The Collective Trust Game: An Online Group Adaptation of the Trust Game Based on the HoneyComb Paradigm

The Collective Trust Game: An Online Group Adaptation of the Trust Game Based on the HoneyComb Paradigm

Published on: October 20, 2022

WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control

WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control

Published on: August 15, 2020

Related Experiment Videos

Last Updated: Jan 4, 2026

The HoneyComb Paradigm for Research on Collective Human Behavior

The HoneyComb Paradigm for Research on Collective Human Behavior

Published on: January 19, 2019

The Collective Trust Game: An Online Group Adaptation of the Trust Game Based on the HoneyComb Paradigm

The Collective Trust Game: An Online Group Adaptation of the Trust Game Based on the HoneyComb Paradigm

Published on: October 20, 2022

WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control

WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control

Published on: August 15, 2020

Area of Science:

Evolutionary Game Theory
Computational Biology
Artificial Intelligence

Background:

Previous research explored strategy evolution in prediction games.
The role of cooperation and competition in driving complexity remains an active area of investigation.
Existing complexity metrics may not adequately differentiate true complexity from genetic bloat.

Purpose of the Study:

To investigate the impact of cooperative and competitive interactions on the evolution of complex strategies.
To extend existing models to noisy game environments.
To introduce and validate a novel metric for measuring evolutionary complexity.

Main Methods:

Development of a new organism and mutation model for noisy prediction games.
Introduction of a novel complexity metric.
Comparative analysis of the novel metric against simpler metrics like raw strategy size.

Main Results:

A combination of cooperation and competition most effectively promotes the growth of strategy complexity.
The novel complexity metric successfully distinguishes genuine complexity from genetic bloat.
Prior findings on the importance of mixed interactions are confirmed in noisy game settings.

Conclusions:

Mixed cooperative and competitive dynamics are crucial for evolving complex strategies.
The developed complexity metric offers a more accurate assessment of evolutionary complexity.
This work provides a refined framework for studying strategy evolution in complex environments.