Peace: Definitions, Models, and AI Interventions

• Peace is defined as a constructive state achieved through intergroup reciprocity, dialogue, and shared experiences, evidenced by initiatives like astronomy outreach and media language analysis.
• Research operationalizes peace via local governance structures, sequential conflict resolution methods, and machine learning techniques that assess intergroup dynamics.
• Quantitative models capture peace as a stable equilibrium through resource allocation, network dynamics, and predictive metrics derived from news language and social feedback.

Peace, in the arXiv literature considered here, is treated as more than the mere absence of war. It appears as a positive condition of intergroup reciprocity, dialogue, and stable coexistence; as a local or system-level equilibrium that can fail under uncertainty, polarization, or adverse geography; as a quantity inferred from media language; and as an operational target of AI systems for moderation, mediation, and reflection on media consumption. At the same time, “PEACE” is also repeatedly reused as an acronym for technically unrelated systems in astronomy, robotics, software engineering, and biomedical machine learning, indicating that the term functions both as a substantive concept and as a naming convention in research practice (Francesca et al., 2018, Morrison et al., 2022, Prasad et al., 2024).

1. Peace as reciprocity, dialogue, and shared perspective

One strand of the literature defines peace through practices that reduce prejudice, widen perspective, and create meaningful contact across divided communities. “Columba-Hypatia: Astronomy for Peace” presents a grass-roots astronomy outreach project in Cyprus organized by GalileoMobile and the Association for Historical Dialogue and Research (AHDR). Its explicit goals are to inspire curiosity about science and the Universe, use astronomy as a tool for peace and non-violence, bring together Greek-Cypriot and Turkish-Cypriot communities, reduce prejudices and misunderstandings, promote meaningful communication, encourage global citizenship, and help children and adults reflect on their place on Earth and in Cyprus. The project operates in a context where the two communities have lived separated for over 50 years, and where interaction and cooperation remain rare despite border crossings opening in 2003. Its methods include mono-communal school visits, bi-communal activity days in the UN-controlled buffer zone at the Home for Cooperation, use of the UNAWE Earth Ball to foreground the artificiality of man-made borders, and mixed-group exercises such as “Building a Cyprus Golden Record” and “Building Constellations in 3D.” Participation was reported at approximately 190 school children, 20 teachers, 100 youth, 150 members of the general public, and about 4300 documentary views; the paper reports enthusiasm, curiosity about the other community, and improved understanding and empathy, while also noting that it does not provide a formal quantitative impact evaluation (Francesca et al., 2018).

A related but computationally framed literature operationalizes peace through intergroup reciprocity. “Classifying Peace in Global Media Using RAG and Intergroup Reciprocity” defines Positive Intergroup Reciprocity (PIR) as “Intergroup tolerance, respect, kindness, help, or support,” and Negative Intergroup Reciprocity (NIR) as “Intergroup intolerance, disrespect, aggression, obstruction, or hindrance.” Peace is then treated as the relative prevalence of PIR over NIR in media articles. The system analyzes 6000 articles per country and computes the percentage of articles closer to PIR than to NIR, producing a normalized country-level measure; reported values range from 100% for New Zealand to 0% for Kenya. The paper’s central claim is that peace is difficult to detect through simple keyword or sentiment methods because intergroup dynamics are relational and often implicit, so theory-grounded retrieval is used to interpret ordinary news articles through peace-studies concepts (Lian et al., 2024).

Taken together, these works depict peace as a positive social relation rather than a residual category. A recurrent misconception is that peace is exhausted by the nonoccurrence of overt violence. The cited studies instead treat peace as something actively constructed through shared experiences, perspective shifts, tolerance, and supportive reciprocity. A plausible implication is that peace-building interventions may be strongest when they create low-threat, jointly intelligible activities rather than merely suppress hostile acts.

2. Peace as local settlement, autonomy, and mediation support

A second literature treats peace as something assembled through local arrangements rather than imposed first as a complete national compact. “Step by Step to Peace in Syria” argues that Syria’s violence is structured by ethnic geography and that a purely national solution is unlikely to be workable in the short term. The paper associates high conflict probability with ethnic group patches of roughly 20–60 km in diameter and uses a simplified visual method with circles of about 20 km diameter on an ethnic map to identify likely violence regions in the northeast, north, west, southwest, and Deir az-Zur. Its proposed mechanism is local autonomy through subnational boundaries and safe zones, potentially at the level of governorates, districts, or subdistricts, with natural barriers such as mountains, rivers, and lakes sometimes helping to support those boundaries. The intervention logic is explicitly sequential: identify a local conflict area, negotiate or support local governance structures, define subnational boundaries, establish a safe zone, allow recovery and normalization, and use successful cases as precedents. The paper states that “the safety of the populace should not and need not be a hostage for the national solution” (Parens et al., 2016).

A more process-oriented contribution appears in “Supporting peace negotiations in the Yemen war through machine learning,” which examines how mediators can manage fragmented, data-rich negotiations. The study uses rough notes from 6 sessions in 2018 and 8 sessions in 2019, totaling 177,789 words, together with more than 30 detailed meeting reports and an internal “comprehensive analysis.” Three support functions are developed: knowledge management and data preparation; issue extraction; and measurement of party distances. Predefined issue extraction uses query-driven topic modeling with GloVe (glove.6B, 300-dimensional vectors) and cosine similarity thresholds from 0.4 up to 0.6, while latent issue extraction compares LDA and NMF and reports that NMF produced better results. Party distances are estimated using bert-base-uncased embeddings and cosine similarity. The paper emphasizes that these tools do not replace human mediators; rather, they help reconstruct issue evolution, identify bottlenecks, explore convergence and divergence, and support agenda setting under meaningful human control (Arana-Catania et al., 2022).

These works converge on a shared point: peace processes are not only normative projects but also organizational and analytical ones. This suggests that local governance design, issue tracking, and structured interpretation of negotiations can be as important as elite bargaining formulas. It also qualifies the common assumption that peace is principally a single end-state agreement; in these papers it is a staged, data-bearing process with multiple scales of implementation.

3. Peace as equilibrium, threshold, and strategic regime

Formal work in economics, network science, and dynamical systems models peace as a stable but contingent regime. In “Peace in the Face of Uncertainty: Resource Allocation with Stochastic Armaments,” a government proposes a transfer rule $\beta \in [0,1]$ to a rebel group, after which rebel armaments receive an additive shock $\epsilon_R \in [\underline a,\overline a]$ with uncertainty summarized by $a=\overline a-\underline a$. The transfer interval $[\beta_G^+,\beta_R^-]$ is central: when it is nonempty, any $\beta$ in that interval is accepted by both sides for every shock realization, so peace is guaranteed. The paper’s main comparative static is non-monotonic. When uncertainty is sufficiently low, the government chooses the maximal peace-guaranteeing transfer, $\beta^*=\beta_R^-$. When uncertainty becomes larger, but still below the point where guaranteed peace is impossible, the government switches to a riskier interior choice with $\beta^*>\beta_R^-$. In that high-uncertainty regime, $\Pr(\text{war})$ is increasing in $a$ and total welfare $W$ is decreasing in $\epsilon_R \in [\underline a,\overline a]$0 (Taylor, 2024).

In “Transitions between peace and systemic war as bifurcations in a signed network dynamical system,” peace is a stable equilibrium of a nonlinear signed network whose ties evolve under a dyad-specific restoring force and a bounded structural balance force. The peace-to-war transition occurs through a saddle-node bifurcation with threshold

$\epsilon_R \in [\underline a,\overline a]$1

where $\epsilon_R \in [\underline a,\overline a]$2 is structural balance sensitivity, $\epsilon_R \in [\underline a,\overline a]$3 is the dyadic restoring strength, and $\epsilon_R \in [\underline a,\overline a]$4 is the leading eigenvalue of the dyadic-bias matrix. The simulations reported in the paper exhibit peace only for $\epsilon_R \in [\underline a,\overline a]$5, bistability for $\epsilon_R \in [\underline a,\overline a]$6, and war only for $\epsilon_R \in [\underline a,\overline a]$7, together with hysteresis and critical slowing down. The argument is that polarized dyadic structure raises $\epsilon_R \in [\underline a,\overline a]$8 and thereby lowers the threshold at which peace becomes unstable (Morrison et al., 2022).

“The Spatial Ecology of War and Peace” gives peace a spatial-network interpretation. In the authors’ global interaction network of cities, degree centrality captures embeddedness in dense cores, while betweenness centrality captures brokerage across bottlenecks or “fuzzy cultural boundaries.” The paper reports threshold-like behavior: zones with $\epsilon_R \in [\underline a,\overline a]$9 typically experience less than 1 attack/year and are typically more than 1200 km from the nearest major conflict zone, whereas zones with $a=\overline a-\underline a$0 satisfy $a=\overline a-\underline a$1 and are usually within 150 km of existing conflict zones. Their combined predictor is strategic centrality,

$a=\overline a-\underline a$2

for which the best-fit relationship between predicted attacks and centrality has adjusted $a=\overline a-\underline a$3, while logistic regression raises predictive performance to about 0.92 adjusted $a=\overline a-\underline a$4 (Guo et al., 2016).

“Peace through bribing” approaches peace as a pre-conflict settlement in an all-pay auction environment. It distinguishes peaceful equilibrium, peace implementable, robust peaceful equilibrium, and peace securable. Its central negative result is that peace security is impossible in the bribing model: no peaceful equilibrium can be guaranteed for every belief system. The paper also proves that separating equilibria do not exist and that any non-peaceful equilibrium has at most two on-path bribes. In the requesting model, by contrast, peace security is possible under the conditions specified in the paper (Lu et al., 2021).

Across these models, peace is not modeled as default quiescence. It is a bounded region in parameter space, a guaranteed-acceptance interval, a low-polarization attractor, or an incentive-compatible settlement. A plausible implication is that peace often fails not only because actors prefer war in the abstract, but because uncertainty widens, network structure polarizes, or belief-robust settlements do not exist.

4. Peace as a measurable property of news language

Several papers ask whether peace leaves a recoverable signature in media language. “Word differences in news media of lower and higher peace countries revealed by natural language processing and machine learning” uses approximately 724,000 English-language news articles from the NOW corpus published between January 2010 and September 2020. After excluding Pakistan and South Africa for insufficient English-language volume, the analysis uses 18 countries and five indices—Global Peace Index, Positive Peace Index, World Happiness Index, Fragile States Index, and Human Development Index—rescaled to 0–100. Countries are classified as lower-peace if they fall into the lowest third in at least 3 of the 5 indices, higher-peace if they fall into the highest third in at least 3 of the 5 indices, and intermediate-peace otherwise. The two-class model trained only on extreme groups performs very strongly, with random forest cross-validation accuracy 0.960 and logistic regression cross-validation accuracy 1.000. Logistic regression probabilities are then repurposed as a continuous measure,

$a=\overline a-\underline a$5

where $a=\overline a-\underline a$6. Reported higher-peace indicators include words such as “time,” “like,” “game,” “play,” “good,” “team,” “people,” “new,” “work,” “help,” and “community,” while lower-peace indicators include “state,” “government,” “country,” “court,” “law,” “police,” “security,” “president,” “general,” and “election.” The paper is explicit that the result is correlational, English-only, and sensitive to disagreement among peace indices, especially for intermediate countries (Liebovitch et al., 2023).

“Words that Represent Peace” uses a different English-language corpus: about 2,000,000 LexisNexis articles from 20 countries over 2010–2020. It averages several peace and development indices—Global Peace Index, Positive Peace Index, Human Development Index, World Happiness Index, Fragile States Index, Inclusiveness Index, and Gini Coefficient—to label countries, then reduces each country’s vocabulary to the top 1,000 words and normalizes counts as

$a=\overline a-\underline a$7

Using a leave-one-country-out strategy and Optuna for hyperparameter tuning, the paper reports 100% precision, 100% recall, and 100% accuracy for Logistic Regression and SVM, and 100% precision, 95% recall, and 95% accuracy for Decision Tree and Random Forest. Its thematic interpretation is that higher-peace news is characterized by finance, daily activities, and health, while lower-peace news is characterized by politics, government, and legal issues (Prasad et al., 2024).

“Neural Networks Measure Peace Levels from News Data similar to Peace Indices” shifts attention from topical words to structure and style. Using the NOW corpus with about 1,000 articles per country across 20 countries, the paper compares Doc2Vec with ChromaDB-managed embeddings based on all-MiniLM-L6-v2 with dimension $a=\overline a-\underline a$8, and evaluates a 1D CNN against k-NN. It reports that the neural model preserves ordinal peace relationships and correlates strongly with the Positive Peace Index, with $a=\overline a-\underline a$9, $[\beta_G^+,\beta_R^-]$0, $[\beta_G^+,\beta_R^-]$1, $[\beta_G^+,\beta_R^-]$2, and $[\beta_G^+,\beta_R^-]$3. The negative sign is expected because higher PPI corresponds to lower peace, while the neural output increases with peace (Lara-Martínez et al., 25 Mar 2026).

These studies collectively argue that peace is not visible only through explicitly pacific vocabulary. It is also encoded in what topics dominate coverage, in whether everyday social and economic life crowds out coercive institutional language, and in latent stylistic structure. They also delimit the claim. The relevant papers repeatedly note English-language bias, corpus dependence, imperfect agreement among peace indices, and the fact that strong classification accuracy does not by itself establish a causal theory of peacefulness.

5. Peace-oriented AI: explanation, counter-speech, and reflective feedback

Another cluster of work uses AI not only to measure peace-related phenomena but also to intervene in communicative environments. “PEACE 2.0: Grounded Explanations and Counter-Speech for Combating Hate Expressions” extends an earlier web-based NLP system from “detect and explain” to “detect, explain, and respond.” It combines a fine-tuned BERT hate-speech classifier with a Retrieval-Augmented Generation pipeline grounded in a curated human-rights knowledge base containing 32,792 documents from the United Nations Digital Library, Eur-Lex, and the European Agency for Fundamental Rights, spanning 2000–2025 and totaling 3,173,630 tokenized paragraphs. Input messages are encoded with BGE-M3; FAISS performs inner-product similarity search and retrieves the top-3 most relevant passages with deduplication; the passages are summarized and then used to condition explanation or counter-speech generation. Supported LLMs are Mistral-7B-Instruct-v0.3, Llama-3.1-8B-Instruct, and CommandR (c4ai-command-r7b-12-2024). Evaluation uses 100 explanations and 100 counter-speech responses rated by three trained annotators on Fluency, Informativeness, Persuasiveness, Soundness, and Specificity, all on a 1–5 Likert scale, together with Distinct-3, Sentence-BERT semantic similarity, perplexity, faithfulness to retrieved evidence, and NLI-based entailment/contradiction using roberta-large-nli. The paper reports that RAG consistently improves explanations and counter-speech, especially for implicit hate, with substantial to perfect Krippendorff’s alpha and statistically significant Wilcoxon signed-rank tests (Damo et al., 19 Feb 2026).

“Measuring and Fostering Peace through Machine Learning and Artificial Intelligence” extends this logic from moderation support to media-diet intervention. For news, it uses approximately 700,000 NOW articles from 18 countries, converts articles to 1,536-dimensional embeddings with text-embedding-3-small, and evaluates three neural architectures: a CNN, a feed-forward network, and a revised CNN. On the NOW dataset, the reported test accuracies are 97.24%, 97.48%, and 96.99%, respectively. On a separate Capstone Peace Speech dataset of 600,000 news articles from 16 countries, per-article transfer accuracies fall to 72.81%, 72.47%, and 69.91%, but country-level aggregation correctly classifies every country. The same paper reports a transfer failure from written news to 22 YouTube transcripts, where the news-trained models label 95–100% of videos as high-peace. To address this, the paper defines five social dimensions for video analysis—compassion–contempt, news–opinion, prevention–promotion, order–creativity, and nuance–simplistic—and compares GoEmotions-based scoring with LLMs on 52 expert-annotated videos. GoEmotions reaches only weak correlation on compassion–contempt, with $[\beta_G^+,\beta_R^-]$4, whereas modern LLMs achieve correlations up to $[\beta_G^+,\beta_R^-]$5, with Gemini 3 Pro Preview showing a notable improvement of +0.317 on the Nuance dimension. These models are integrated into the Chrome extension MirrorMirror, which provides real-time feedback about the peacefulness of YouTube videos (Gilda et al., 8 Jan 2026).

The conceptual move here is significant. Peace promotion is no longer limited to diplomacy, education, or ex post content labeling; it is also pursued through inspectable explanations, evidence-grounded counter-speech, and user-facing feedback loops. The papers are cautious, however. The YouTube evaluation set is small, the extension is not backed by causal evidence of behavior change, and model failures under domain shift are explicitly documented.

6. “PEACE” as acronym: technical reuse beyond peace studies

Outside peace research proper, “PEACE” is frequently used as an acronym for technically unrelated systems. In those cases the term names a method rather than the social condition.

System	Expansion	Core function
PEACE (Lee et al., 2013)	Pulsar Evaluation Algorithm for Candidate Extraction	Post-analysis ranking of pulsar survey candidates
PEACE (Bong et al., 2023)	Prompt Engineering Automation for CLIPSeg Enhancement	Safe-landing zone segmentation for UAV descent
Peace (Ren et al., 20 Oct 2025)	Project-level code Efficiency optimization through Automatic Code Editing	Dependency-aware code efficiency optimization
PEACE (Liu et al., 1 May 2026)	Pediatric-Adult ECG Alignment via Cross-modal Enhancement	Adult-to-pediatric ECG transfer for diagnosis

In radio astronomy, PEACE is a deterministic scoring framework for post-analysis processing of pulsar survey candidates. It extracts six quality factors—$[\beta_G^+,\beta_R^-]$6, $[\beta_G^+,\beta_R^-]$7, $[\beta_G^+,\beta_R^-]$8, $[\beta_G^+,\beta_R^-]$9, $\beta$0, and $\beta$1—combines them into a single score, and ranks candidates without requiring prior training data sets. The paper states that PEACE significantly increases the pulsar identification rate by a factor of about 50 to 1000 and had been directly responsible for the discovery of 47 new pulsars, 5 of which are millisecond pulsars (Lee et al., 2013).

In aerial robotics, PEACE is a prompt-engineering layer for CLIPSeg-based safe landing. It automatically generates image-conditioned prompts, supports safe descent operations from 100 meters to altitudes as low as 20 meters, and is reported to improve successful identification of safe landing zones from 57% to 92% relative to standard CLIP and CLIPSeg prompting methods (Bong et al., 2023).

In software engineering, Peace is a hybrid framework for project-level code efficiency optimization through automatic code editing. It integrates dependency-aware optimizing function sequence construction, valid associated edits identification, and efficiency optimization editing iteration, and is evaluated on PeacExec, a benchmark of 146 real-world optimization tasks from 47 GitHub Python projects. The reported headline results are a 69.2% correctness rate (pass@1), +46.9% opt rate, and 0.840 speedup (Ren et al., 20 Oct 2025).

In biomedical machine learning, PEACE is a structured cross-modal alignment framework for adult-to-pediatric ECG transfer. Using tri-axial clinical semantic decomposition, label-query feature extraction, and curriculum-gated optimization, it achieves 59.39%, 79.03%, and 90.89% AUC on ZZU-pECG under zero-shot, 50-shot, and full fine-tuning settings, and 96.65% AUC on the shared PTB-XL label space (Liu et al., 1 May 2026).

This multiplicity does not imply conceptual continuity among the systems. It instead suggests that “PEACE” has become a productive acronymic label across domains. A plausible implication is that the term now operates in two distinct research registers: as a substantive object of peace and conflict studies, and as a compact, favorable mnemonic in technical system naming.