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

Social Foundations of Self I: Play and Game01:24

Social Foundations of Self I: Play and Game

197
The development of self in children is deeply rooted in social interactions, mainly through stages of play and structured games. These stages, outlined by sociologist George Herbert Mead, illustrate how children progressively learn to understand and adopt social roles, forming a cohesive sense of self.The Play Stage: Imitation and Simple Role-TakingIn the early years of childhood, the play stage is characterized by imitative behavior, where children engage in role-playing based on familiar...
197
Cross-reactivity00:42

Cross-reactivity

32.9K
Overview
32.9K
Reactivity of Enols01:18

Reactivity of Enols

4.0K
Enols are a class of compounds where a hydroxyl group is attached to a carbon–carbon double bond, which implies that it is a vinyl alcohol. A carbonyl compound with an α hydrogen undergoes keto–enol tautomerism and remains in equilibrium with its tautomer, the enol form. Usually, the keto tautomer is present in a higher concentration than the enol tautomer due to the higher bond energy of C=O compared to C=C. Moreover, the direction of the keto–enol equilibrium is...
4.0K
Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

2.5K
Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
2.5K
Reactivity of Enolate Ions01:23

Reactivity of Enolate Ions

3.3K
Enolate ions are formed by the acid–base reaction of a carbonyl compound with a base. This leads to deprotonation of the α hydrogen atom, leading to a resonance-stabilized enolate ion where one of the contributing structures is an oxyanion, which imparts additional stability. Therefore, the proton on the α carbon is more acidic in nature than that of other sp3-hybridized C–H bonds but less acidic than those in O–H bonds where the negative charge in the conjugate...
3.3K
Radical Reactivity: Overview01:11

Radical Reactivity: Overview

2.6K
Radicals, the highly reactive species, gain stability by undergoing three different reactions. The first reaction involves a radical-radical coupling, in which a radical combines with another radical, forming a spin‐paired molecule. The second reaction is between a radical and a spin‐paired molecule, generating a new radical and a new spin‐paired molecule. The third reaction is radical decomposition in a unimolecular reaction, forming a new radical and a spin‐paired...
2.6K

You might also read

Related Articles

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

Sort by
Same author

Large language models instantiate evolutionarily robust strategies of cooperation.

PNAS nexus·2026
Same author

Cooperation conflicts with equality when allocating public goods.

Nature·2026
Same author

Age distinguishes selection from causation in cancer genomes.

Nature genetics·2026
Same author

Reply to Sacco: Complete spaces as outcomes of evolutionary optimization.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

Payoff equivalence and complete strategy spaces of direct reciprocity.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

Revealing the Drivers Underlying Distinct Evolutionary Trajectories in Lung Adenocarcinoma.

bioRxiv : the preprint server for biology·2026
Same journal

Computational modelling distinguishes diverse contributors to aneurysmal progression in the Marfan aorta.

Proceedings. Mathematical, physical, and engineering sciences·2025
Same journal

Inferring the shape of data: a probabilistic framework for analysing experiments in the natural sciences.

Proceedings. Mathematical, physical, and engineering sciences·2023
Same journal

The Elbert range of magnetostrophic convection. I. Linear theory.

Proceedings. Mathematical, physical, and engineering sciences·2022
Same journal

Soft wetting with (a)symmetric Shuttleworth effect.

Proceedings. Mathematical, physical, and engineering sciences·2022
Same journal

The quantum theory of time: a calculus for q-numbers.

Proceedings. Mathematical, physical, and engineering sciences·2022
Same journal

Integrable nonlinear evolution equations in three spatial dimensions.

Proceedings. Mathematical, physical, and engineering sciences·2022
See all related articles

Related Experiment Video

Updated: Jan 26, 2026

The Collective Trust Game: An Online Group Adaptation of the Trust Game Based on the HoneyComb Paradigm
06:18

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

Published on: October 20, 2022

2.5K

Reactive learning strategies for iterated games.

Alex McAvoy1, Martin A Nowak1

  • 1Program for Evolutionary Dynamics, Harvard University, 1 Brattle Square, Suite 6, Cambridge, MA 02138, USA.

Proceedings. Mathematical, Physical, and Engineering Sciences
|April 23, 2019
PubMed
Summary
This summary is machine-generated.

Reactive learning strategies in iterated games offer a powerful way to restrict outcomes. These strategies, corresponding to memory-one strategies, define feasible payoff regions that are subsets of their memory-one counterparts.

Keywords:
adaptive strategyiterated gamememory-one strategysocial dilemma

More Related Videos

Using Practice Testing, Public Speaking, and Source Monitoring to Examine the Influences of Learning Strategies and Stress on Episodic Memory
07:59

Using Practice Testing, Public Speaking, and Source Monitoring to Examine the Influences of Learning Strategies and Stress on Episodic Memory

Published on: June 14, 2019

8.4K
Stereoacuity Improvement using Random-Dot Video Games
06:25

Stereoacuity Improvement using Random-Dot Video Games

Published on: January 14, 2020

15.0K

Related Experiment Videos

Last Updated: Jan 26, 2026

The Collective Trust Game: An Online Group Adaptation of the Trust Game Based on the HoneyComb Paradigm
06:18

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

Published on: October 20, 2022

2.5K
Using Practice Testing, Public Speaking, and Source Monitoring to Examine the Influences of Learning Strategies and Stress on Episodic Memory
07:59

Using Practice Testing, Public Speaking, and Source Monitoring to Examine the Influences of Learning Strategies and Stress on Episodic Memory

Published on: June 14, 2019

8.4K
Stereoacuity Improvement using Random-Dot Video Games
06:25

Stereoacuity Improvement using Random-Dot Video Games

Published on: January 14, 2020

15.0K

Area of Science:

  • Game Theory
  • Computational Economics
  • Decision Sciences

Background:

  • Iterated games involve complex strategy interactions.
  • Memory-one strategies, dependent on the previous round's outcome, are commonly used.
  • Characterizing feasible payoffs is crucial for understanding game dynamics.

Purpose of the Study:

  • To introduce and analyze 'reactive learning strategies' in iterated games.
  • To establish the relationship between reactive learning strategies and memory-one strategies.
  • To determine the feasible payoff regions for these strategies.

Main Methods:

  • Defining linear reactive learning strategies and their correspondence to memory-one strategies.
  • Analyzing the feasible payoff regions generated by these strategies.
  • Proving that the feasible payoff region of a reactive learning strategy is a subset of its corresponding memory-one strategy's region.

Main Results:

  • Every linear reactive learning strategy corresponds to a memory-one strategy, and vice versa.
  • Evaluating the feasible payoff region against a memory-one strategy requires checking at most 11 other strategies.
  • The feasible payoff region of a reactive learning strategy is a subset of the region of its corresponding memory-one strategy.

Conclusions:

  • Reactive learning strategies are more restrictive than memory-one strategies.
  • These strategies provide a powerful mechanism for limiting outcomes in iterated games.
  • The characterization of feasible payoffs is simplified by considering reactive learning strategies.