Correction: Lessons Learned: Creating Tutorials to Teach Agent-Based Modelling to Archaeologists

Agent-based models (ABMs) are widely used in biology to understand how individual actions scale into emergent population behavior. Modelers employ sensitivity analysis (SA) algorithms to quantify input parameters' impact on model outputs, however, it is hard to perform SA for ABMs due to their computational and complex nature. In this work, we develop the Simulate, Summarize, Reduce, Cluster, and Analyze (SSRCA) methodology, a machine-learning based pipeline designed to facilitate SA for ABMs. In particular, SSRCA can achieve the following tasks for ABMs: 1) identify sensitive model parameters, 2) reveal common output model patterns, and 3) determine which input parameter values generate these patterns. We use an example ABM of tumor spheroid growth to showcase how SSRCA identifies four common patterns from the ABM and the parameter regions that generate these outputs. Additionally, we compare the SA results between SSRCA and the popular Sobol' Method and find that SSRCA's identified sensitive parameters are robust to the choice of model descriptors while Sobol's are not. This analysis could streamline data-driven tasks, such as parameter estimation, for ABMs by reducing parameter space. While we highlight these results with an ABM on tumor spheroid formation, the SSRCA Methodology is broadly applicable to biological ABMs.

Enggano Island is situated above the southern segment of the Sunda megathrust, making it highly vulnerable to earthquake and tsunami hazards. In remote coastal villages, such as Kaana, the lack of adequate evacuation infrastructure presents significant challenges for disaster risk reduction. This study aims to evaluate tsunami evacuation strategies using an agent-based modeling approach implemented in a three-dimensional simulation environment. A purposive sampling survey involving 83 residents was conducted to collect socio-demographic data, tsunami awareness, preparedness levels, and evacuation preferences. These inputs were used to calibrate agent behavior and movement patterns to reflect realistic community dynamics in the simulation. The model simulates multiple evacuation configurations to examine survival rates and evacuation times under different spatial layouts, building distributions, and shelter capacity assumptions. Results show that horizontal evacuation via a single inland route leads to severe congestion and low survival outcomes, with only 8.2% of agents reaching safety within ten minutes. In contrast, the addition of vertical evacuation buildings significantly enhances evacuation performance, yielding survival rates above 90% under all conditions. Even when shelter capacity is limited to 70% of its full design, over 93% of agents are still able to evacuate successfully, although with increased delays. Vertical-only evacuation produces stable performance with average completion times of approximately five minutes. These findings emphasize the importance of integrating vertical shelters in strategic locations, optimizing route accessibility, and adapting building capacity to physical and demographic constraints. This study contributes to tsunami risk mitigation planning by offering empirical insights into evacuation dynamics in isolated island environments such as Enggano Island, Indonesia.

Bats face a complex navigation challenge when emerging from densely populated roosts, where vast numbers take off at once in dark, confined spaces. Each bat must avoid collisions with walls and conspecifics while locating the exit, all amidst overlapping acoustic signals. This crowded environment creates the risk of acoustic jamming, in which the calls of neighboring bats interfere with echo detection, potentially obscuring vital information. Despite these challenges, bats navigate these conditions with remarkable success. Although bats have access to multiple sensory cues, here, we focused on whether echolocation alone could provide sufficient information for orientation under such high-interference conditions. To explore whether and how they manage this challenge, we developed a sensorimotor model that mimics the bats' echolocation behavior under high-density conditions. Our model suggests that the problem of acoustic jamming may be less severe than previously assumed. Frequent calls with short inter-pulse intervals (IPI) increase the sensory input flow, allowing integration of echoic information across multiple calls. When combined with simple movement-guidance strategies-such as following walls and avoiding nearby obstacles-this accumulated information enables effective navigation in dense acoustic environments. Together, these findings demonstrate a plausible mechanism by which bats may overcome acoustic interference and underscore the role of signal redundancy in supporting robust echolocation-based navigation. Beyond advancing our understanding of bat behavior, they also offer valuable insights for swarm robotics and collective movement in complex environments.

ML-ABC: Machine-learning assisted Approximate Bayesian Computation for efficient calibration of agent-based models for pandemic outbreak analysis.

Known unknowns and the osteological paradox: Why bioarchaeology needs agent-based models.

There is a power law of joint communicative effort and it reflects communicative work.

Value allocation mechanism for multi-agent data sharing in digital innovation network: Agent-based modeling

Agent-based modeling of residential solar adoption in subsidized energy markets

In a world of drastic climatic and ecological changes, our knowledge of how the environment influenced hominin behaviour is of the utmost importance. Archaeology plays a key role in this domain, as it is the only discipline that studies empirical evidence of past societies’ responses to environmental change. Computational models generating predictions about past climatic and ecological conditions are vital for understanding the archaeological record and how these factors shaped the dispersal of hominins out of Africa and into Eurasia during the Early and early Middle Pleistocene. In this paper, various models for past reconstructions of climatic and ecological conditions and simulation techniques are presented to provide an overview of the diverse approaches, possibilities, advantages and constraints of using computational reconstructions in archaeological research. Focusing on studies of hominin dispersals out of Africa and into Eurasia during the Early and early Middle Pleistocene, this paper discusses the links between environmental factors and hominin dispersal behaviour. The use of simulation techniques to represent hominin populations, such as cellular automata or agent-based modelling, can contribute to connecting small-scale environment-induced influences on hominins to large-scale patterns, supported by ecological theories of species survival and spatial behaviour. Collectively, these approaches provide an elaborate foundation for understanding environmental influences on past hominin dispersals.

Abstract In increasingly personalized and opaque media environments, traditional methods in communication research struggle to capture the dynamics of algorithm-mediated systems. This paper reviews the rise of agent-based approaches as a methodological response to the observability crisis and mechanistic dilemma posed by algorithmic personalization. We mainly focus on two complementary paradigms: agent-based testing (ABT) and agent-based modeling (ABM). While ABT provides empirical measurement of algorithmic behavior, ABM formalizes theoretical assumptions and enables scenario-based exploration. We further highlight their integration into an iterative loop of measurement, modeling, prediction, and validation, and discuss how recent advances in large language models transform agents into adaptive, human-like entities. Finally, we address methodological and ethical challenges, proposing agent-based approaches as a systematic framework to study, explain, and anticipate the societal consequences of algorithm-mediated communication.

Agent-Based Models (ABMs) have proven to be an effective tool for describing and predicting the dynamics of respiratory infections and forecasting future outbreaks and have helped health organizations control the disease by developing effective intervention strategy. The use of ABMs is accompanied by very high computational cost, which limits their use in real time. Replacing ABMs with machine learning-based models that can replicate the output or couple the two models together is a solution to the computational cost problem. This paper proposes a machine learningbased surrogate model to simulate an ABM simulating the spread of respiratory infection in Saint Petersburg to reduce simulation time and maintain equivalent accuracy in estimates. The research was based on evaluating the performance of a set of machine learning models under different approaches as surrogate models to use in place of ABM. Methods for generating ABM output chains were compared and evaluated through experiments using single-model approaches or ensemble approaches as a predictive model for each time step in the output (independent multi-output and regression chaining) or hybrid models between agent-based and machine learning. The results indicated that there are several models capable of replicating the simulation output sequence of the ABM with a slight superiority of eXtreme Gradient Boosting within the regression chaining approach. In the hybrid approach, the Long Short Term Memory model with the first values of the output sequence within the feature space outperformed the other models in obtaining more accurate results and achieved the lowest Mean Absolute Error and Root Mean Square Error.

Evaluating select factors and mechanisms influencing meat consumption in Baltimore City: an agent-based modeling study.

Nature and nurture perspectives of the gender-equality paradox frequently talk past each other because they do not share underlying assumptions regarding which sex differences are inherent. We overcome this limitation by investigating the consequences of a sex difference both perspectives agree is inherent: physical size. We leveraged equestrian sports as a unique test case in which males and females compete together and in which smaller physical size provides a competitive advantage. Findings showed that gender equality is a stronger predictor of female representation in equestrian sports and that countries high in gender equality have the most extreme overrepresentation of female equestrians, compared with table tennis, judo, swimming, or sailing (total n = 442,453, 128+ countries in each sport). Furthermore, we show that the pattern of findings is inconsistent with the nurture perspective of the gender-equality paradox by simulating its assumptions using agent-based modeling and comparing these results to historical data of Olympic equestrians.

Quantification of circular economy (CE) is essential for effective implementation, yet also fundamentally challenging, because it is inherently complex, featuring multiple interactions and system-level dynamicity. Two main approaches of systems thinking, commonly used to model complexities in intricate systems, are: system dynamics (SD), providing a top-down, macroscopic view; and agent-based modelling and simulation (ABMS), offering a bottom-up, microscopic perspective. Here we conducted a Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Scoping Reviews (PRISMA-ScR) review, examining 60 studies applying SD or ABMS to CE, across sectors such as bio-based materials, construction and industrial symbiosis. Both methods capture aspects of circularity's feedback loops and time evolution, but they are often used in isolation in the absence of integrated platforms along with concerns over computational costs. This limits their capacity to comprehensively model internal dynamics at multiple scales and provide system-wide decision support. Few studies explore the potential of combining SD and ABMS or attempt to integrate them with static tools, such as life-cycle assessment and multi-criteria decision analysis. Standardised metrics and operational holistic evaluation tools incorporating economic, environmental, technical and social sustainability aspects are missing - especially with the latter. A more unified and comprehensive systems approach to support informed decisions on circularity would improve evidence-based policymaking and empower wider industrial adoption.

Decoding agent-based models supports students’ mechanistic and causal reasoning about scientific phenomena

Water resource management poses persistent challenges for economic policy analysis due to heterogeneous users, institutional fragmentation, and dynamic feedback between human behavior and hydrological systems. Agent-based modeling (ABM) has increasingly been applied to address these limitations, yet its use in water policy analysis remains fragmented across domains and modeling traditions. This paper reviews the application of ABM in water resources management from an applied economics perspective. Focusing on four major policy domains—individual water demand, basin-scale allocation and coordination, groundwater over-extraction, and water pollution regulation—we synthesize how ABM has been used to represent behavioral heterogeneity, institutional interaction, and micro–macro feedback. The review highlights the value of ABM as a policy laboratory for exploring mechanisms, trade-offs, and distributional consequences under decentralized and second-best conditions, while also identifying key methodological challenges related to validation, integration, and behavioral specification. We conclude by discussing implications for future research and policy practice, emphasizing the complementary role of ABM alongside econometric evidence and optimization benchmarks in ex ante water policy assessment.

Understanding collective behavior in social networks is a central challenge in social science, data science, and computational modeling. Classical network and agent-based models often assume linear interactions and independent decision-making, yet real-world social systems exhibit nonlinear dynamics, rapid opinion shifts, polarization, and context-dependent responses that are difficult to predict. This study explores how quantum-inspired modeling frameworks can enhance the analysis of collective behavior by incorporating principles such as superposition, interference, and probabilistic state transitions. Rather than treating individual opinions as fixed, quantum-inspired approaches model social agents as existing in multiple potential cognitive or behavioral states that evolve through interaction and contextual influence. Through qualitative synthesis of literature in network science, behavioral modeling, and quantum-inspired computation, this paper examines how these frameworks capture emergent phenomena such as opinion cascades, synchronization, and abrupt phase transitions in social dynamics. The analysis highlights applications in misinformation diffusion, social polarization, collective decision-making, and adaptive coordination. The findings suggest that quantum-inspired models offer a flexible and scalable approach for representing uncertainty, ambiguity, and contextual dependence in social systems. Ultimately, this study positions quantum-inspired modeling as a promising tool for advancing the predictive understanding of collective behavior in complex social networks.

ABSTRACT This study aims to construct a new paradigm for leisure research based on Agent-Based Modelling (ABM), in response to the limitations of traditional positivist and interpretivist approaches in explaining complex, dynamic leisure behaviours and emergent social processes. It systematically reviews the epistemological foundations and methodological characteristics of ABM and demonstrates its suitability and application logic in relation to research questions in leisure studies. An exemplar model is then presented to illustrate the model-building process and the specification of core mechanisms in leisure-related ABM. Furthermore, this paper proposes a modelling framework that integrates Large Language Models (LLMs) with Point of Interest (POI) data to enhance the rationality of behavioural rule specification and spatial environment representation. The study shows that ABM can capture individual heterogeneity, local interaction processes, and the emergence of macroscopic leisure phenomena within a unified framework, thereby facilitating a shift in leisure research from static correlation analysis to mechanism-oriented dynamic simulation. The contribution of this paper lies in providing a reusable framework for generating leisure behaviours, validating the feasibility of applying ABM to leisure research, and offering a modelling reference for future data-driven ABM studies in leisure contexts.

This study investigates the multi-locality and multi-temporal characteristics of mobility destinations in Zanjan, Iran, throughout a typical day. Existing approaches often overlook critical geographical concepts, including the influence of multiple motivational factors on destination choice behavior, the clustering of destinations, and the spatiotemporal dynamics of preferred destinations. To address these gaps, Agent-Based Modeling (ABM) was employed to simulate individual daily flows to preferred destinations. An integrated pattern recognition approach combining machine learning clustering (k-means), hotspot analysis, and 3D mapping was utilized to facilitate visual analytics of individual destination choices, with special emphasis on applications for transportation planning. Four optimal destination clusters were identified, with hotspot analysis revealing a concentration of preferred destinations in Cluster 1, located within the Central Business District (CBD), suggesting a monocentric spatial structure. Temporal analysis demonstrated that destination clusters exhibit dynamic spatial and temporal changes over the course of the day. These findings provide new insights into managing travel behavior and offer practical implications for urban planning and transportation policy regarding individuals’ daily movement strategies.