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Why agent based models don't work?

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Agent-based models (ABMs) face challenges due to the intractability of likelihood functions and non-differentiability of mathematical operations, hindering their real-world applicability . ABMs can be opaque, impeding critical assessment and reproducibility, jeopardizing study conclusions . Despite being a powerful tool for modeling complex systems, ABMs struggle with chaotic and sensitive macro-level social phenomena due to the lack of empirical data and the difficulty in forecasting such phenomena . Additionally, when dealing with epistemic uncertainty, ABMs may not provide detailed insights into expected outcomes and trends, as demonstrated in a battleship simulation model comparing Monte Carlo and interval implementations . These challenges highlight the need for improved inference methods, standardization in methodology, and consideration of multiple analysis approaches to enhance the effectiveness of ABMs.

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Open access•Posted Content•DOI Bayesian calibration of differentiable agent-based models 24 May 2023	Agent-based models face challenges due to intractable likelihood functions and non-differentiability, hindering real-world applicability. Approximate Bayesian inference methods are being developed to address these limitations.
Book Chapter•DOI Investigating social phenomena with agent-based models 31 Mar 2023	Not addressed in the paper.
Journal Article•DOI Quo vadis, agent-based modelling tools? Aisling J. Daly, Lander De Visscher, Jan M. Baetens, Bernard De Baets - Show less +3 more 01 Sep 2022-Environmental Modelling and Software 1 Citations	Not addressed in the paper.
Journal Article•DOI Bayesian calibration of differentiable agent-based models Arnau Quera-Bofarull, Ayush Chopra, Ani Calinescu, Michael Wooldridge, Joel Dyer - Show less +4 more 24 May 2023-arXiv.org 1 Citations	Not addressed in the paper.
Journal Article•DOI Agent-based models under uncertainty Vladimir Stepanov, Scott Ferson - Show less +1 more 14 Jul 2023-F1000Research	Not addressed in the paper.

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How develop an agent-based model?5 answersTo develop an agent-based model, one must first understand that agent-based modeling (ABM) is a powerful tool for studying complex systems by simulating the interactions of individual agents to observe emergent phenomena on a macroscopic level. The process involves creating decentralized agents with specific behaviors that collectively shape the system's overall behavior. Initial populations of agents are established based on relevant data, such as statistical information about human populations in specific regions, along with algorithms defining their actions and characteristics. ABMs require careful consideration of complexity levels to balance computational burden and model applicability. Additionally, choosing the appropriate ABM tool is crucial, as various open-source options exist with different features, ease of use, and efficiency for developing and running simulations.

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What are the benefits of agent-based modeling for simulating human behavior?5 answersAgent-based modeling (ABM) offers several benefits for simulating human behavior. ABMs provide an intuitive and powerful framework for studying social dynamics by modeling the interactions of individuals from the perspective of each individual. ABMs can simulate and forecast the dynamics of social systems and can also be used to solve optimization problems, such as decision-making processes. ABMs allow for the efficient solution of multi-objective optimization problems involving complex, high-dimensional dynamical systems, such as ABMs. ABMs can model behavior at multiple timescales, which substantially improves the prediction of future behavior compared to methods that do not consider multiple timescales. ABMs are effective modeling techniques for energy markets, allowing for the exploration of system outcomes and the introduction of market complexities and policy scenarios. ABMs provide a safe and ethical way to study human societies and make discoveries that enlighten us about ourselves.

What is agent based modelling?3 answersAgent-based modeling is a computational dynamic modeling technique that aims to understand the behavior of complex systems by studying the interactions between individual agents and their environment. It offers advantages such as simpler representation of heterogeneity, the ability to capture network and spatial context, and consideration of individual history within the model. In contrast, compartmental models, another modeling technique, offer faster development time, lower computational requirements, and concise mathematical formulation. Agent-based models are particularly useful for studying social dynamics and can be used to simulate and forecast the behavior of systems. They can also be used to solve optimization problems, although the computational cost can be high. Overall, agent-based modeling provides a flexible and scalable approach to modeling complex systems, allowing for a more realistic representation of real-world phenomena.

Explain about agent-based model and how to use it to simulate team work in team of teams?5 answersAgent-based models (ABMs) are useful for simulating team work in a team of teams. ABMs simulate the behavior and interactions of individual agents within a system, allowing for a detailed analysis of complex systems. In the context of team work, ABMs can be used to model the behavior of individual team members and their interactions within a larger team of teams. By specifying the behavior and decision-making processes of individual agents, ABMs can simulate how teams collaborate, communicate, and coordinate their actions. This can provide insights into the dynamics of team work, identify potential issues, and inform strategies for improving team performance. ABMs can also be used to explore different scenarios and test the impact of various factors on team outcomes. Overall, ABMs offer a powerful tool for understanding and simulating team work in complex organizational settings.

What are agent-based models?3 answersAgent-based models (ABMs) are computational models that simulate the actions and interactions of agents within a system. These models represent systems as populations of interacting components governed by rules. ABMs can be used to explore the emergent behavior of complex systems, where the behavior of the system depends on the micro-level behavior of the individuals. ABMs have been applied in various fields, including social sciences, biology, and epidemiology. They provide a methodology to study systems of interacting, adaptive, diverse actors and can generate outcomes such as equilibrium points, equilibrium distributions, cycles, randomness, or complex patterns. ABMs offer the potential to advance social sciences and help us better understand our complex world.

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