From evolutionary ecosystem simulations to computational models of human behavior

Bentley, Peter J.; Lim, Soo Ling

doi:10.1002/wcs.1622

Cited by 4 publications

(3 citation statements)

References 85 publications

(85 reference statements)

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“…Computational simulations empower clinicians to assess diverse treatment options, diminish subjectivity in treatment planning, and enhance clinical outcomes for individual patients [3][4][5][6][7]. These virtual models and experiments emulate real-world phenomena using mathematical algorithms and computer software, facilitating the exploration, comprehension, and prediction of the behavior of complex systems or processes that may be difficult, costly, unsafe, or not ethical to study directly [8,9]. However, despite the significant promises of this method, the research community has only made limited progress in validating its predictions due to the scarcity of direct in vivo measurements [10], which are limited for technological and ethical reasons.…”

Section: Introductionmentioning

confidence: 99%

A Sensorized 3D-Printed Knee Test Rig for Preliminary Experimental Validation of Patellar Tracking and Contact Simulation

Michaud,

Mouzo,

Dopico

et al. 2024

Sensors

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Experimental validation of computational simulations is important because it provides empirical evidence to verify the accuracy and reliability of the simulated results. This validation ensures that the simulation accurately represents real-world phenomena, increasing confidence in the model’s predictive capabilities and its applicability to practical scenarios. The use of musculoskeletal models in orthopedic surgery allows for objective prediction of postoperative function and optimization of results for each patient. To ensure that simulations are trustworthy and can be used for predictive purposes, comparing simulation results with experimental data is crucial. Although progress has been made in obtaining 3D bone geometry and estimating contact forces, validation of these predictions has been limited due to the lack of direct in vivo measurements and the economic and ethical constraints associated with available alternatives. In this study, an existing commercial surgical training station was transformed into a sensorized test bench to replicate a knee subject to a total knee replacement. The original knee inserts of the training station were replaced with personalized 3D-printed bones incorporating their corresponding implants, and multiple sensors with their respective supports were added. The recorded movement of the patella was used in combination with the forces recorded by the pressure sensor and the load cells, to validate the results obtained from the simulation, which was performed by means of a multibody dynamics formulation implemented in a custom-developed library. The utilization of 3D-printed models and sensors facilitated cost-effective and replicable experimental validation of computational simulations, thereby advancing orthopedic surgery while circumventing ethical concerns.

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Section: Introductionmentioning

confidence: 99%

A Sensorized 3D-Printed Knee Test Rig for Preliminary Experimental Validation of Patellar Tracking and Contact Simulation

Michaud,

Mouzo,

Dopico

et al. 2024

Sensors

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“…This technique provides insights into the cell groups’ emerging behaviour and collective effects. Further, computational modeling can be applied to population, ecological, and evolutionary contexts ( Creanza et al , 2017 , Muhammad-Kah et al , 2019 , Bentley and Lim, 2022 , Cosme et al , 2023 ). Ecological models help us understand how different species interact, their population dynamics, and the stability of entire ecosystems ( Evans, 2012 , van den Berg et al , 2022 ).…”

Section: Introductionmentioning

confidence: 99%

Perspectives on computational modeling of biological systems and the significance of the SysMod community

Puniya,

Verma,

Damiani

et al. 2024

Bioinformatics Advances

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Motivation In recent years, applying computational modeling to systems biology has caused a substantial surge in both discovery and practical applications and a significant shift in our understanding of the complexity inherent in biological systems. Results In this perspective article, we briefly overview computational modeling in biology, highlighting recent advancements such as multi-scale modeling due to the omics revolution, single-cell technology, and integration of artificial intelligence and machine learning approaches. We also discuss the primary challenges faced: integration, standardization, model complexity, scalability, and interdisciplinary collaboration. Lastly, we highlight the contribution made by the Computational Modeling of Biological Systems (SysMod) Community of Special Interest (COSI) associated with the International Society of Computational Biology (ISCB) in driving progress within this rapidly evolving field through community engagement (via both in person and virtual meetings, social media interactions), webinars, and conferences. Availability and implementation Additional information about SysMod is available at https://sysmod.info.

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“…There has also been increasing research that uses evolutionary ABMs to explore and understand human interactions (Bentley and Lim, 2022). Nikolic and Dijkema (2010) developed an evolutionary modeling process for creating evolving, complex ABMs to understand the evolution of large-scale sociotechnical systems such as infrastructures for energy, water, and transport.…”

Section: Introductionmentioning

confidence: 99%

Kill chaos with kindness: Agreeableness improves team performance under uncertainty

Lim

Bentley

Peterson

et al. 2023

Collective Intelligence

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Teams are central to human accomplishment. Over the past half-century, psychologists have identified the Big-Five cross-culturally valid personality variables: Neuroticism, Extraversion, Openness, Conscientiousness, and Agreeableness. The first four have shown consistent relationships with team performance. Agreeableness (being harmonious, altruistic, humble, and cooperative), however, has demonstrated a non-significant and highly variable relationship with team performance. We resolve this inconsistency through computational modelling. An agent-based model (ABM) is used to predict the effects of personality traits on teamwork, and a genetic algorithm is then used to explore the limits of the ABM in order to discover which traits correlate with best and worst performing teams for a problem with different levels of uncertainty (noise). New dependencies revealed by the exploration are corroborated by analyzing previously unseen data from one of the largest datasets on team performance to date comprising 3698 individuals in 593 teams working on more than 5000 group tasks with and without uncertainty, collected over a 10-year period. Our finding is that the dependency between team performance and Agreeableness is moderated by task uncertainty. Combining evolutionary computation with ABMs in this way provides a new methodology for the scientific investigation of teamwork, making new predictions, and improving our understanding of human behaviors. Our results confirm the potential usefulness of computer modelling for developing theory, as well as shedding light on the future of teams as work environments are becoming increasingly fluid and uncertain.

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From evolutionary ecosystem simulations to computational models of human behavior

Cited by 4 publications

References 85 publications

A Sensorized 3D-Printed Knee Test Rig for Preliminary Experimental Validation of Patellar Tracking and Contact Simulation

A Sensorized 3D-Printed Knee Test Rig for Preliminary Experimental Validation of Patellar Tracking and Contact Simulation

Perspectives on computational modeling of biological systems and the significance of the SysMod community

Kill chaos with kindness: Agreeableness improves team performance under uncertainty

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