Physica A: Statistical Mechanics and its Applications最新文献

Analysis of mobility discrepancies within urban agglomerations using an extended PageRank algorithm in time-varying multimodal networks 基于扩展PageRank算法的时变多模式网络中城市群交通差异分析

IF 3.1 3区物理与天体物理

Physica A: Statistical Mechanics and its Applications Pub Date : 2025-10-22 DOI: 10.1016/j.physa.2025.131060

Zhiwei Zhang , Hiroe Ando , Yige Wang , Tianlei Zhu , Xin Yang

{"title":"Analysis of mobility discrepancies within urban agglomerations using an extended PageRank algorithm in time-varying multimodal networks","authors":"Zhiwei Zhang , Hiroe Ando , Yige Wang , Tianlei Zhu , Xin Yang","doi":"10.1016/j.physa.2025.131060","DOIUrl":"10.1016/j.physa.2025.131060","url":null,"abstract":"<div><div>Understanding human mobility is essential for transportation planning. However, most existing studies focus on individual mobility prediction or single-mode evaluation, while the time-varying and multimodal features of urban mobility remain largely overlooked. To address these limitations, this study proposed a novel characterization approach that simplifies dynamic mobility networks into static snapshots and incorporates heterogeneous temporal correlations and modal synergies. Based on network characteristics, an extended PageRank algorithm was proposed to evaluate regional importance. Kumamoto (Japan) was used as a case study to validate the effectiveness of the proposed analytical framework. The results suggest that the importance of peripheral areas increases with homebound trips during the evening peak, and static characterization leads to the underestimation of these areas. Furthermore, simplified characterization of interlayer links also results in an inaccurate assessment of peripheral areas. These findings reevaluate the importance of peripheral areas in urban agglomerations, providing novel insights for transportation planning. More importantly, the proposed analytical framework could identify influential nodes in time-varying networks more accurately.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":"681 ","pages":"Article 131060"},"PeriodicalIF":3.1,"publicationDate":"2025-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145340967","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Interpretable knowledge tracing via fine-grained multi-feature attribution 基于细粒度多特征属性的可解释知识跟踪

IF 3.1 3区物理与天体物理

Physica A: Statistical Mechanics and its Applications Pub Date : 2025-10-22 DOI: 10.1016/j.physa.2025.131068

Hang Liang , Yi-Fei Wen , Yajun Du , Xiaoliang Chen , Tao Zhou , Yan-Li Lee

{"title":"Interpretable knowledge tracing via fine-grained multi-feature attribution","authors":"Hang Liang , Yi-Fei Wen , Yajun Du , Xiaoliang Chen , Tao Zhou , Yan-Li Lee","doi":"10.1016/j.physa.2025.131068","DOIUrl":"10.1016/j.physa.2025.131068","url":null,"abstract":"<div><div>With the growth of massive educational data and the rapid advancement of artificial intelligence technologies, knowledge tracing has become increasingly important for assessing students’ knowledge states. Existing deep learning-based knowledge tracing models have achieved increasingly high predictive accuracy. However, they fail to capture significant features with explicit educational significance, which limits educators’ understanding, trust, and practical use of the diagnostic results. In this paper, we propose a Fine-Grained <strong>M</strong>ulti-<strong>F</strong>eature <strong>A</strong>ttribution <strong>I</strong>nterpretable <strong>K</strong>nowledge <strong>T</strong>racing model (<strong>MFA-IKT</strong> for short). It integrates educational theories with students’ learning behavior pattern, modeling fine-grained features of questions in terms of difficulty and discrimination and capturing the multidimensional dynamic features of students on knowledge mastery and ability profile. A Tree-Augmented Naive Bayes structure is adopted to construct the dependencies between the evidence features and the prediction outcomes. Experiments on five real-world datasets show that our model outperforms all baselines, including deep learning-based models, achieving average improvements of 9.28% in AUC and 9.99% in RMSE. Further analysis reveals that question-side features have a greater impact than student-side features. Among the fine-grained question features, discriminative features significantly enhance the model’s predictive performance. This study, through modeling interpretable features and attributing prediction outcomes, presents an explainable intelligent tutoring framework for personalized education, comprising “learning outcome prediction <span><math><mo>→</mo></math></span> feature attribution <span><math><mo>→</mo></math></span> instructional intervention suggestions”.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":"681 ","pages":"Article 131068"},"PeriodicalIF":3.1,"publicationDate":"2025-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145340964","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

From equality to diversity: A bottom-up approach for hierarchy growth 从平等到多样性：层级增长的自下而上方法

IF 3.1 3区物理与天体物理

Physica A: Statistical Mechanics and its Applications Pub Date : 2025-10-22 DOI: 10.1016/j.physa.2025.131062

Agnieszka Czaplicka, Janusz A. Hołyst

{"title":"From equality to diversity: A bottom-up approach for hierarchy growth","authors":"Agnieszka Czaplicka, Janusz A. Hołyst","doi":"10.1016/j.physa.2025.131062","DOIUrl":"10.1016/j.physa.2025.131062","url":null,"abstract":"<div><div>Hierarchical topology stands as a fundamental property of many complex systems. In this work, we present a simple yet insightful model that captures hierarchy growth from bottom to top. Our model incorporates two key dynamic processes: the emergence of local leaders through promotions, where successful agents advance to higher hierarchical levels by attracting followers, and the natural degradation of agents to the lowest level. From an initial flat structure where all agents occupy the bottom level, the system evolves toward a stationary state characterized by an exponential distribution of agents across levels—a pattern remarkably similar to those observed in diverse real-world hierarchies, from hunter-gatherer societies and mammalian groups to online communities. Notably, while the average hierarchy level and the fraction of ground-level agents remain independent of system size, the maximum height of the hierarchy grows logarithmically with the total number of agents. In the stationary state, agents maintain a significantly smaller number of followers compared to their peak influence at the promotion moment. Results from numerical simulations are supported by analytical solutions derived based on the rate equations.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":"681 ","pages":"Article 131062"},"PeriodicalIF":3.1,"publicationDate":"2025-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145340895","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Prediction of hazardous transmission lines after power grid cascading failures using the node and edge attributed graph edge-attention residual network 基于节点边缘属性图的危险输电线路级联故障预测

IF 3.1 3区物理与天体物理

Physica A: Statistical Mechanics and its Applications Pub Date : 2025-10-22 DOI: 10.1016/j.physa.2025.131066

Miao Chen , Yanli Zou

{"title":"Prediction of hazardous transmission lines after power grid cascading failures using the node and edge attributed graph edge-attention residual network","authors":"Miao Chen , Yanli Zou","doi":"10.1016/j.physa.2025.131066","DOIUrl":"10.1016/j.physa.2025.131066","url":null,"abstract":"<div><div>With the increasing integration of renewable energy and complex hybrid AC/DC grid topologies, power systems face heightened cascading failure risks under N-k contingencies. Following such failures, topological reconfiguration and load adjustments can cause some transmission lines to enter a “subcritical overload” state,where power flow exceeds stability limits without triggering protection leading to latent faults and secondary collapse risks. To tackle this, this paper proposes a Node and Edge Attributed Graph Edge-Attention Residual Network (NEA-GEAT-Res) for predicting potentially overloaded lines. Using IEEE test cases with random and clustered faults, we simulate cascading failures via an AC-Cascading Failure Model (AC-CFM). Post-failure, lines are classified as failed, normal, or hazardous. Based on the NEA-GNN framework, our model introduces cross-layer residual connections to preserve initial features and mitigate over-smoothing, alongside an edge attention mechanism that dynamically weights critical line information. Experiments show that NEA-GEAT-Res achieves F1-scores of 96.42 % (IEEE 39-bus) and 84.59 % (IEEE 118-bus), improving over baseline NEA-GNN by 16.79 % and 14.87 %, and outperforming other mainstream models. Notably, adding topological features benefits baseline models (3 %–11 % F1 improvement) but not NEA-GEAT-Res, indicating our model effectively captures dynamic grid characteristics through residual and attention mechanisms. This work reveals GNN feature sensitivity in hazardous line prediction and suggests hybrid feature modeling avenues, providing a high-accuracy solution for proactive defense after cascading failures.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":"681 ","pages":"Article 131066"},"PeriodicalIF":3.1,"publicationDate":"2025-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145366075","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Sampling effects on recurrence microstates distribution 抽样对递归微态分布的影响

IF 3.1 3区物理与天体物理

Physica A: Statistical Mechanics and its Applications Pub Date : 2025-10-22 DOI: 10.1016/j.physa.2025.131065

Felipe Eduardo Lopes da Cruz , Gilberto Corso , Thiago de Lima Prado , Sergio Roberto Lopes , Norbert Marwan , Jürgen Kurths

{"title":"Sampling effects on recurrence microstates distribution","authors":"Felipe Eduardo Lopes da Cruz , Gilberto Corso , Thiago de Lima Prado , Sergio Roberto Lopes , Norbert Marwan , Jürgen Kurths","doi":"10.1016/j.physa.2025.131065","DOIUrl":"10.1016/j.physa.2025.131065","url":null,"abstract":"<div><div>The method of recurrence plots (RP) is a valuable tool in time series analysis. Recurrence microstate analysis is a useful concept, which allows a deep characterization of the time series dynamics. How to sample the microstates and a robust sampling strategy are central questions in recurrence microstate analysis. We study different sampling strategies: with overlapping or not, using the full RP or just half RP. Three time series are employed in our analysis: the uniform random noise, the logistic map and an EEG experimental time series. We compare microstate distributions from the sampling strategies using the Jensen–Shannon distance. In addition, we estimate the recurrence entropy for the analyzed sampling strategies for variable microstate samplings. We conclude that the overlapping sampling shows a superior performance compared to the non-overlapping strategy. This study investigates criteria to determine a robust microstate sampling size by analyzing the asymptotic behavior of recurrence entropy, entropy differences, and the standard deviation of an ensemble time series. Additionally, the Jensen–Shannon distance is combined with recurrence entropy to establish a reliable sampling size, showing that while the optimal sampling size depends on the time series dynamics and microstate size, a rule of thumb for microstates with size <span><math><mrow><mi>k</mi><mo>=</mo><mn>3</mn></mrow></math></span> is a sample size of around the lower bound of <span><math><mrow><mi>O</mi><mrow><mo>(</mo><mn>1</mn><msup><mrow><mn>0</mn></mrow><mrow><mn>4</mn></mrow></msup><mo>)</mo></mrow></mrow></math></span> for most series.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":"681 ","pages":"Article 131065"},"PeriodicalIF":3.1,"publicationDate":"2025-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145366076","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Reputation-based strategy persistence and environmental feedback promote cooperation in spatial snowdrift games 基于声誉的策略持久性和环境反馈促进了空间雪堆游戏中的合作

IF 3.1 3区物理与天体物理

Physica A: Statistical Mechanics and its Applications Pub Date : 2025-10-22 DOI: 10.1016/j.physa.2025.131061

Jinxiu Pi , Chun Wang , Jin Cai , Wei Tang

{"title":"Reputation-based strategy persistence and environmental feedback promote cooperation in spatial snowdrift games","authors":"Jinxiu Pi , Chun Wang , Jin Cai , Wei Tang","doi":"10.1016/j.physa.2025.131061","DOIUrl":"10.1016/j.physa.2025.131061","url":null,"abstract":"<div><div>In complex human social environments, individuals tend to emulate those who achieve both high benefits and high reputations, rather than nice guys who maintain good reputations despite obtaining low benefits. Typically, individual reputation does not vary uniformly with the persistence of her/his strategies. Meanwhile, environmental reputation emerges from these individual reputations and in turn influences the persistence of individual strategies. Motivated by these facts, we incorporate strategy persistence and environmental feedback into the spatial snowdrift game to investigate how individual reputation affects behavioral choices and promotes social cooperation under these dual mechanisms. Specifically, we first define high-benefit individuals as those whose payoffs exceed the community average, then characterize the nonlinear dynamics of individual reputation using an exponential function, and finally construct an environmental feedback function based on local and global reputations. Simulation results demonstrate that enhanced individual reputation reinforced by high benefits and strategic persistence facilitates the propagation of cooperative behaviors. Moreover, increasing emphasis on local neighbor reputation while reducing consideration of global population reputation in environmental feedback mechanisms significantly promotes cooperation levels within the system.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":"681 ","pages":"Article 131061"},"PeriodicalIF":3.1,"publicationDate":"2025-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145340894","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Interaction-aware trajectory prediction for heterogeneous agents in shared spaces 共享空间中异构agent的交互感知轨迹预测

IF 3.1 3区物理与天体物理

Physica A: Statistical Mechanics and its Applications Pub Date : 2025-10-17 DOI: 10.1016/j.physa.2025.131054

Junfei Zhang , Yingchun Fan , Fei Hui , Erlong Tan , Xingkai Zhou

{"title":"Interaction-aware trajectory prediction for heterogeneous agents in shared spaces","authors":"Junfei Zhang , Yingchun Fan , Fei Hui , Erlong Tan , Xingkai Zhou","doi":"10.1016/j.physa.2025.131054","DOIUrl":"10.1016/j.physa.2025.131054","url":null,"abstract":"<div><div>Trajectory prediction in shared spaces represents a fundamental challenge for autonomous systems, requiring accurate forecasting of heterogeneous traffic participants including pedestrians, cyclists, and vehicles. Although deep learning methods have advanced trajectory forecasting, most existing approaches either neglect heterogeneity among agents or focus solely on interactions during the observed history, failing to account for dynamically evolving interactions that may emerge in future time steps. To address these challenges, we propose a novel encoder–decoder framework that strategically integrates cascade spatial–temporal interaction modeling in the encoder and a cross-LSTM decoder, explicitly capturing interactions in the observed history while leveraging the cross-LSTM to account for dynamically emerging interactions throughout the prediction horizon. Experiments on two datasets demonstrate that our approach achieves superior prediction accuracy(ADE/FDE) and lower collision rates compared to strong baselines. Factor analysis and ablation studies validate the effectiveness of each core module and further reveal that reducing the frequency of interaction modeling in the decoder improves both prediction accuracy and computational efficiency. Our findings provide valuable insights for designing more effective and efficient architectures for trajectory prediction in shared space.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":"680 ","pages":"Article 131054"},"PeriodicalIF":3.1,"publicationDate":"2025-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145334425","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Congestion mitigation method for scenic area roads based on bidirectional pedestrian flow optimization and control 基于双向人流优化控制的景区道路拥堵缓解方法

IF 3.1 3区物理与天体物理

Physica A: Statistical Mechanics and its Applications Pub Date : 2025-10-17 DOI: 10.1016/j.physa.2025.131055

Yuanyuan Zhang , Liang Li , Yanbin Han , Sijie Niu , Zihao Dong , Qingtao Hou

{"title":"Congestion mitigation method for scenic area roads based on bidirectional pedestrian flow optimization and control","authors":"Yuanyuan Zhang , Liang Li , Yanbin Han , Sijie Niu , Zihao Dong , Qingtao Hou","doi":"10.1016/j.physa.2025.131055","DOIUrl":"10.1016/j.physa.2025.131055","url":null,"abstract":"<div><div>Pedestrian counterflow is one of the main causes of congestion in scenic areas, usually resulting from the improper allocation of bidirectional pedestrian flow on roads. To solve this problem, we propose a bidirectional pedestrian flow optimization strategy aimed at mitigating pedestrian counterflow, increasing pedestrian touring efficiency, and enhancing scenic area utilization. First, two assessment indicators, modified pedestrian traffic efficiency (MPTE) and spatial distribution dispersion (SDD), are introduced to assess road conditions. Then, a pedestrian flow optimization method is presented to adjust the number and direction of pedestrians by optimizing the bidirectional traffic ratios on the road. Finally, we build a pedestrian tour simulation model based on the reciprocal velocity obstacle (RVO) to validate our method. The experimental results indicate that the average tour time of tourists is shortened by 33.70 %, and the road balance is increased by 44.79 %, which provides some suggestions for tourist management in scenic areas.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":"681 ","pages":"Article 131055"},"PeriodicalIF":3.1,"publicationDate":"2025-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145341017","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Double asymmetric multifractal detrended fluctuation analysis (DA-MFDFA): Extending asymmetric multifractal methods for exogenous time series 双非对称多重分形无趋势波动分析（DA-MFDFA）：外生时间序列的非对称多重分形方法的扩展

IF 3.1 3区物理与天体物理

Physica A: Statistical Mechanics and its Applications Pub Date : 2025-10-17 DOI: 10.1016/j.physa.2025.131027

Werner Kristjanpoller

{"title":"Double asymmetric multifractal detrended fluctuation analysis (DA-MFDFA): Extending asymmetric multifractal methods for exogenous time series","authors":"Werner Kristjanpoller","doi":"10.1016/j.physa.2025.131027","DOIUrl":"10.1016/j.physa.2025.131027","url":null,"abstract":"<div><div>This paper introduces Double Asymmetric Multifractal Detrended Fluctuation Analysis (DA-MFDFA), a novel extension of traditional multifractal methods designed to capture the joint influence of price trends and trading volume on financial return dynamics. By integrating an exogenous volume-based asymmetry, DA-MFDFA provides a richer framework to analyze complex market behavior, especially under extreme bullish and bearish conditions. Applying DA-MFDFA to a comprehensive sample of S&P 500 stocks over multiple rolling windows, we identify distinct multifractal patterns characterized by varying degrees of long memory and reversal effects across different market regimes. Our findings demonstrate that strong volume-reinforced trends are associated with heightened reversal behaviors and increased market complexity, while standard trends exhibit greater persistence. These results carry important implications for portfolio management and trading strategies, enabling more adaptive risk assessment and regime-sensitive decision-making. The flexibility of the DA-MFDFA framework further allows incorporation of additional external factors, broadening its applicability in financial modeling and other fields. This study advances multifractal analysis by highlighting the critical role of volume dynamics in shaping market efficiency and return behavior.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":"680 ","pages":"Article 131027"},"PeriodicalIF":3.1,"publicationDate":"2025-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145364270","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Modeling metaorder impact with a Non-Markovian Zero Intelligence model 用非马尔可夫零智能模型建模元序影响

IF 3.1 3区物理与天体物理

Physica A: Statistical Mechanics and its Applications Pub Date : 2025-10-17 DOI: 10.1016/j.physa.2025.131056

Adele Ravagnani , Fabrizio Lillo

{"title":"Modeling metaorder impact with a Non-Markovian Zero Intelligence model","authors":"Adele Ravagnani , Fabrizio Lillo","doi":"10.1016/j.physa.2025.131056","DOIUrl":"10.1016/j.physa.2025.131056","url":null,"abstract":"<div><div>Devising models of the limit order book that realistically reproduce the market response to exogenous trades is extremely challenging and fundamental in order to test trading strategies. We propose a novel explainable model for small tick assets, the Non-Markovian Zero Intelligence, which is a variant of the well-known Zero Intelligence model. The main modification is that the probability of limit orders’ signs (buy/sell) is not constant but is a function of the exponentially weighted mid-price return, representing the past price dynamics, and can be interpreted as the reaction of traders with reservation prices to the price trend. With numerical simulations and analytical arguments, we show that the model predicts a concave price path during a metaorder execution and to a price reversion after the execution ends, as empirically observed. We analyze in-depth the mechanism at the root of the arising concavity, the components which constitute the price impact in our model, and the dependence of the results on the two main parameters, namely the time scale and the strength of the reaction of traders to the price trend.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":"681 ","pages":"Article 131056"},"PeriodicalIF":3.1,"publicationDate":"2025-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145340966","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0