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Defensive Strategies and Handling Paths in Intimate Relationship Conflicts: A Dynamic Game Model From the Perspective of Emotional Regulation.

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Paper

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The authors' code

Python · 98 lines · 3.1 KB · Apache-2.0

  1. """compare timings across 2 benchmarks."""
  3. import pickle
  5. import numpy as np
  6. import pandas as pd
  8. filename1 = "timings_1"
  9. filename2 = "timings_2"
  11. with open(f"{filename1}.pickle", "rb") as handle:
  12. timings_1 = pickle.load(handle) # noqa: S301
  14. with open(f"{filename2}.pickle", "rb") as handle:
  15. timings_2 = pickle.load(handle) # noqa: S301
  18. def bootstrap_percentage_change_confidence_interval(data1, data2, n=1000):
  19. """Calculate the percentage change and perform bootstrap to estimate the confidence interval.
  21. Args:
  22. data1: benchmark dataset 1
  23. data2: benchmark dataset 2
  24. n: bootstrap sample size
  26. Returns:
  27. float, mean, and lower and upper bound of confidence interval.
  28. """
  29. change_samples = []
  30. for _ in range(n):
  31. sampled_indices = np.random.choice(
  32. range(len(data1)), size=len(data1), replace=True
  33. )
  34. sampled_data1 = np.array(data1)[sampled_indices]
  35. sampled_data2 = np.array(data2)[sampled_indices]
  36. change = 100 * (sampled_data2 - sampled_data1) / sampled_data1
  37. change_samples.append(np.mean(change))
  38. lower, upper = np.percentile(change_samples, [2.5, 97.5])
  39. return np.mean(change_samples), lower, upper
  42. # DataFrame to store the results
  43. results_df = pd.DataFrame()
  46. def performance_emoji(lower, upper):
  47. """Function to determine the emoji based on change and confidence interval."""
  48. if upper < -3:
  49. return "🟢" # Emoji for faster performance
  50. elif lower > 3:
  51. return "🔴" # Emoji for slower performance
  52. else:
  53. return "🔵" # Emoji for insignificant change
  56. # Iterate over the models and sizes, perform analysis, and populate the DataFrame
  57. for model, size in timings_1:
  58. model_name = model.__name__
  60. # Calculate percentage change and confidence interval for init times
  61. (
  62. init_change,
  63. init_lower,
  64. init_upper,
  65. ) = bootstrap_percentage_change_confidence_interval(
  66. timings_1[(model, size)][0], timings_2[(model, size)][0]
  67. )
  68. init_emoji = performance_emoji(init_lower, init_upper)
  69. init_summary = (
  70. f"{init_emoji} {init_change:+.1f}% [{init_lower:+.1f}%, {init_upper:+.1f}%]"
  71. )
  73. # Calculate percentage change and confidence interval for run times
  74. run_change, run_lower, run_upper = bootstrap_percentage_change_confidence_interval(
  75. timings_1[(model, size)][1], timings_2[(model, size)][1]
  76. )
  77. run_emoji = performance_emoji(run_lower, run_upper)
  78. run_summary = (
  79. f"{run_emoji} {run_change:+.1f}% [{run_lower:+.1f}%, {run_upper:+.1f}%]"
  80. )
  82. # Append results to DataFrame
  83. row = pd.DataFrame(
  84. {
  85. "Model": [model_name],
  86. "Size": [size],
  87. "Init time [95% CI]": [init_summary],
  88. "Run time [95% CI]": [run_summary],
  89. }
  90. )
  92. results_df = pd.concat([results_df, row], ignore_index=True)
  94. # Convert DataFrame to markdown with specified alignments
  95. markdown_representation = results_df.to_markdown(index=False, tablefmt="github")
  97. # Display the markdown representation
  98. print(markdown_representation)

compare_timings.py at commit f85955b, under Apache-2.0 · at the source

Overview

Authors: Yiwen Liu1
ORCID iDs: Yiwen Liu
  1. The Education University of Hong Kong Hong Kong China
Institutions: Education University of Hong Kong (Hong Kong SAR China)
Journal: PsyCh journal, volume 15, issue 4, article e70103
Dates: received 22 April 2026; accepted 6 May 2026; published online 18 July 2026; in print August 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1002/pchj.70103 · PMID 42470233 · PMCID PMC13379787 · OpenAlex W7169681904
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: human (organism), cognitive (subfield)
Methods: Spectral & time-frequency, Machine learning, Connectivity, Preprocessing, Statistics, Physiology & signal measures, Smoothing, state filtering, decompositions
Keywords: conflict, defense strategy, dynamic game model, emotional regulation, intimate relationship, processing path
MeSH: Conflict, Psychological*, Emotional Regulation*, Game Theory*, Interpersonal Relations*, Adult, Female, Humans, Male, Models, Psychological (* major topic)
Topic: Attachment and Relationship Dynamics (Social Psychology, Psychology), according to OpenAlex
Citations: not cited yet (Europe PMC); 36 references in the paper

Abstract

This study develops a dynamic game model of intimate relationship conflicts that incorporates emotion regulation mechanisms, translating cognitive reappraisal and expressive suppression into strategic parameters. The model simulates the dynamic evolution of defensive strategies and intervention paths, capturing the continuous interplay between emotional states and conflict behavior choices in realistic relational contexts. Using longitudinal tracking data from 320 couples and agent‐based simulations, we validate the core mechanisms and demonstrate that flexibility in emotion regulation significantly reduces conflict intensity and shortens conflict cycles. When one partner's emotion regulation capacity is limited, power asymmetry intensifies defensive rigidity, highlighting the combined influence of neural coordination, relational fluidity, and micro‐level cultural practices on conflict dynamics. By operationalizing emotion regulation as a computable variable within a formalized game‐theoretic framework, this research bridges theoretical constructs and observable behavior, revealing how neural synchrony and relational context shape the selection and effectiveness of defensive strategies. The findings offer both theoretical and practical contributions: theoretically, the study provides a more integrated understanding of intimate relationship conflicts, emphasizing the joint roles of emotional, relational, and cultural factors in conflict evolution; practically, the model equips clinicians and family intervention practitioners with a structured, quantifiable tool for diagnosing conflict patterns and designing evidence‐based strategies to reduce destructive behaviors and foster cooperative problem‐solving. Overall, this study presents a concise yet comprehensive framework that advances both the conceptualization and empirical analysis of intimate relationship conflicts, offering actionable insights for intervention while highlighting the importance of integrating emotional, neural, and cultural dimensions in research and practice.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above.

projectmesa/mesa

License: Apache-2.0
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Commit: f85955b7ecc3a6d79d8bfaec2d0b406d85c8f0a6, 23 September 2026
Languages: Python (151), Jupyter (13), Shell (1)
Size: 245 files, 165 scripts
Software Heritage: archived
Found in: “Data Availability Statement”
Holds: README, license file, CITATION.cff, environment (pyproject.toml, binder/environment.yml), tests, continuous integration, documentation, 13 notebooks
Tools: NumPy (49 files), pandas (29 files), Matplotlib (14 files), NetworkX (10 files), seaborn (10 files), SciPy (5 files), Pillow (2 files)
Availability: 1 check, the latest on 26 September 2026: the link answers
  • 26 September 2026: the link answers
167 files
At the source: github.com/projectmesa/mesa

The paper's code and data availability statement is in the Data section.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 165 scripts, each with its path and the digest of its content;
  • no match between paragraphs and code yet;
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Data Availability Statement

To enhance transparency and reproducibility, the simulation procedure used in this study follows standard agent‐based modeling practices. Representative implementations of agent‐based simulation frameworks are publicly available in the Mesa open‐source repository (https://github.com/projectmesa/mesa), which provides a Python environment for constructing and analyzing agent‐based models of complex social interactions. The analytical procedures described in this study can be reproduced using this framework together with the parameter settings and experimental design reported in the manuscript.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 1 author, 6 keywords, 9 MeSH terms, 35 references.

Cite

This paper

Liu, Y. (2026). Defensive Strategies and Handling Paths in Intimate Relationship Conflicts: A Dynamic Game Model From the Perspective of Emotional Regulation. PsyCh journal, 15(4), e70103. https://doi.org/10.1002/pchj.70103

BibTeX

@article{liu2026defensive,
author = {Liu, Yiwen},
title = {{Defensive Strategies and Handling Paths in Intimate Relationship Conflicts: A Dynamic Game Model From the Perspective of Emotional Regulation}},
journal = {PsyCh journal},
year = {2026},
month = aug,
volume = {15},
number = {4},
pages = {e70103},
publisher = {Wiley},
issn = {2046-0252},
doi = {10.1002/pchj.70103},
url = {https://doi.org/10.1002/pchj.70103},
pmid = {42470233},
pmcid = {PMC13379787}
}

RIS

TY - JOUR
AU - Liu, Yiwen
TI - Defensive Strategies and Handling Paths in Intimate Relationship Conflicts: A Dynamic Game Model From the Perspective of Emotional Regulation
T2 - PsyCh journal
J2 - Psych J
PY - 2026
DA - 2026/08/01
VL - 15
IS - 4
SP - e70103
SN - 2046-0252
PB - Wiley
DO - 10.1002/pchj.70103
UR - https://doi.org/10.1002/pchj.70103
LA - en
ER -

CSL-JSON

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"container-title": "PsyCh journal",
"author": [
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"family": "Liu",
"given": "Yiwen"
}
],
"container-title-short": "Psych J",
"volume": "15",
"issue": "4",
"page": "e70103",
"DOI": "10.1002/pchj.70103",
"PMID": "42470233",
"PMCID": "PMC13379787",
"ISSN": "2046-0252",
"publisher": "Wiley",
"URL": "https://doi.org/10.1002/pchj.70103",
"language": "en",
"issued": {
"date-parts": [
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1
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]
}
}

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