A comparison of sexual selection versus random selection with respect to extinction and speciation rates using individual based modeling and machine learning

Synergistic Integration Between Machine Learning and Agent-Based Modeling: A Multidisciplinary Review

Agent-based modeling (ABM) involves developing models in which agents make adaptive decisions in a changing environment. Machine-learning (ML) based inference models can improve sequential decision-making by learning agents' behavioral patterns. With the aid of ML, this emerging area can extend traditional agent-based schemes that hardcode agents' behavioral rules into an adaptive model. Even though there are plenty of studies that apply ML in ABMs, the generalized applicable scenarios, frameworks, and procedures for implementations are not well addressed. In this article, we provide a comprehensive review of applying ML in ABM based on four major scenarios, i.e., microagent-level situational awareness learning, microagent-level behavior intervention, macro-ABM-level emulator, and sequential decision-making. For these four scenarios, the related algorithms, frameworks, procedures of implementations, and multidisciplinary applications are thoroughly investigated. We also discuss how ML can improve prediction in ABMs by trading off the variance and bias and how ML can improve the sequential decision-making of microagent and macrolevel policymakers via a mechanism of reinforced behavioral intervention. At the end of this article, future perspectives of applying ML in ABMs are discussed with respect to data acquisition and quality issues, the possible solution of solving the convergence problem of reinforcement learning, interpretable ML applications, and bounded rationality of ABM.

Digital Evolution for Ecology Research: A Review

In digital evolution, populations of computational organisms evolve via the same principles that govern natural selection in nature. These platforms have been used to great effect as a controlled system in which to conduct evolutionary experiments and develop novel evolutionary theory. In addition to their complex evolutionary dynamics, many digital evolution systems also produce rich ecological communities. As a result, digital evolution is also a powerful tool for research on eco-evolutionary dynamics. Here, we review the research to date in which digital evolution platforms have been used to address eco-evolutionary (and in some cases purely ecological) questions. This work has spanned a wide range of topics, including competition, facilitation, parasitism, predation, and macroecological scaling laws. We argue for the value of further ecological research in digital evolution systems and present some particularly promising directions for further research.

Movement rule selection through eco-genetic modeling: Application to diurnal vertical movement

Agent-based, spatially-explicit models that incorporate movement rules are used across ecological disciplines for a variety of applications. However, appropriate movement rules may be difficult to implement due to the complexity of an individual's response to both proximate and ultimate cues, as well as the difficulty in directly assessing how organisms choose to move across their environment. Environmental cues may be complex and dynamic, and therefore, movement responses may require tradeoffs between preferred levels of different environmental variables (e.g., temperature, light level, and prey availability). Here, we present an approach to determine appropriate movement rules by setting them as heritable traits in an eco-genetic modeling framework and allowing movement rules to evolve during the model rather than setting them a priori. We modeled yellow perch, Perca flavescens, movement in a simulated environment and allowed perch to move in response to high-resolution vertical gradients in temperature, dissolved oxygen, light, predators, and prey. Evolving movement rules ultimately increased fish growth and survival over generations in our model, indicating that evolving movement rules led to improved individual performance. We found that emergent movement rules were consistent across trials, with evolved movement rules incorporating different weights of these environmental factors and the most rapid selection on temperature preference. This case study presents a flexible method using eco-genetic modeling to determine appropriate movement rules that can be applied to diverse scenarios in spatially-explicit ecological modeling.