scholarly journals Controlling evolutionary dynamics to optimize microbial bioremediation

2019 ◽  
Author(s):  
Shota Shibasaki ◽  
Sara Mitri
Keyword(s):  
Theoretical Model ◽  
Flow Rate ◽  
Evolutionary Dynamics ◽  
Evolutionary Game ◽  
Environmental Parameters ◽  
Public Goods Game ◽  
Public And Private ◽  
Private Goods ◽  
Practical Applications ◽  
Microbial Bioremediation

AbstractSome microbes have a fascinating ability to degrade compounds that are toxic for humans in a process called bioremediation. Although these traits help microbes survive the toxins, carrying them can be costly if the benefit of detoxification is shared by all surrounding microbes, whether they detoxify or not. Detoxification can thereby be seen as a public goods game, where non-degrading mutants can sweep through the population and collapse bioremediation. Here, we constructed an evolutionary game theoretical model to optimize bioremediation in a chemostat initially containing “cooperating” (detoxifying) microbes. We consider two types of mutants: “cheaters” that do not detoxify, and mutants that become resistant to the toxin through private mechanisms that do not benefit others. By manipulating the concentration and flow rate of a toxin into the chemostat, we identified conditions where cooperators can exclude cheaters that differ in their private resistance. However, eventually, cheaters are bound to invade. To overcome this inevitable outcome and maximize detoxification efficiency, cooperators can be periodically reinoculated into the population. Our study investigates the outcome of an evolutionary game combining both public and private goods and demonstrates how environmental parameters can be used to control evolutionary dynamics in practical applications.

EVOLUTIONARY DYNAMICS OF CLIMATE CHANGE UNDER COLLECTIVE-RISK DILEMMAS

2012 ◽  
Vol 22 (supp01) ◽  
pp. 1140004 ◽  
Author(s):  
FRANCISCO C. SANTOS ◽  
VÍTOR V. VASCONCELOS ◽  
MARTA D. SANTOS ◽  
P. N. B. NEVES ◽  
JORGE M. PACHECO
Keyword(s):  
Climate Change ◽  
Decision Making ◽  
Global Warming ◽  
Theoretical Model ◽  
Small Groups ◽  
Evolutionary Dynamics ◽  
Stochastic Dynamics ◽  
Evolutionary Game ◽  
Collective Risk ◽  
The Commons

Preventing global warming requires overall cooperation. Contributions will depend on the risk of future losses, which plays a key role in decision-making. Here, we discuss an evolutionary game theoretical model in which decisions within small groups under high risk and stringent requirements toward success significantly raise the chances of coordinating to save the planet's climate, thus escaping the tragedy of the commons. We discuss both deterministic dynamics in infinite populations, and stochastic dynamics in finite populations.

scholarly journals Evolutionary dynamics of group interactions on structured populations: a review

2013 ◽  
Vol 10 (80) ◽  
pp. 20120997 ◽  
Author(s):  
Matjaž Perc ◽  
Jesús Gómez-Gardeñes ◽  
Attila Szolnoki ◽  
Luis M. Floría ◽  
Yamir Moreno
Keyword(s):  
Statistical Physics ◽  
Evolutionary Dynamics ◽  
Evolutionary Game ◽  
Evolutionary Games ◽  
Structured Populations ◽  
Public Goods Game ◽  
Group Interactions ◽  
The Public ◽  
Living Organisms ◽  
Class Representative

Interactions among living organisms, from bacteria colonies to human societies, are inherently more complex than interactions among particles and non-living matter. Group interactions are a particularly important and widespread class, representative of which is the public goods game. In addition, methods of statistical physics have proved valuable for studying pattern formation, equilibrium selection and self-organization in evolutionary games. Here, we review recent advances in the study of evolutionary dynamics of group interactions on top of structured populations, including lattices, complex networks and coevolutionary models. We also compare these results with those obtained on well-mixed populations. The review particularly highlights that the study of the dynamics of group interactions, like several other important equilibrium and non-equilibrium dynamical processes in biological, economical and social sciences, benefits from the synergy between statistical physics, network science and evolutionary game theory.

scholarly journals Swords and Plowshares: Property Rights, Collective Action, and Nonstate Governance in the Jewish Community of Palestine 1920–1948

2021 ◽  
pp. 1-15
Author(s):  
DAVID MUCHLINSKI
Keyword(s):  
Property Rights ◽  
Public Goods ◽  
Jewish Community ◽  
Causal Effect ◽  
Public Goods Provision ◽  
Public And Private ◽  
Private Goods ◽  
Agricultural Communities ◽  
Institutional Variation ◽  
Subnational Governance

Developing states lacking a monopoly over the use of force are commonly seen as having failed to live up to the ideal Weberian sovereign type. Yet rather than being a calling card of anarchy, the devolution of important state functions to subnational actors is a rational strategy for developing states to effectively provide important public goods. The case study of the Jewish Community of Palestine demonstrates one instance where subnational communities provided public goods. This study highlights the causal effect of property rights within institutions to drive behavior consistent with the provision of public and private goods. Analyzing temporal and institutional variation across two agricultural communities demonstrates a unique strategy of subnational governance and public goods provision in a developing state. Devolution of public goods provision to subnational actors may be an alternative strategy of governance for developing states that are not yet able to effectively provide important public goods.

scholarly journals Cooperation and competition between pair and multi-player social games in spatial populations

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Attila Szolnoki ◽  
Xiaojie Chen
Keyword(s):  
Public Goods ◽  
Evolutionary Game Theory ◽  
Evolutionary Game ◽  
Pair Interaction ◽  
Public Goods Game ◽  
Point Interactions ◽  
Social Games ◽  
The Public ◽  
Specific Parameter ◽  
Group Members

AbstractThe conflict between individual and collective interests is in the heart of every social dilemmas established by evolutionary game theory. We cannot avoid these conflicts but sometimes we may choose which interaction framework to use as a battlefield. For instance some people like to be part of a larger group while other persons prefer to interact in a more personalized, individual way. Both attitudes can be formulated via appropriately chosen traditional games. In particular, the prisoner’s dilemma game is based on pair interaction while the public goods game represents multi-point interactions of group members. To reveal the possible advantage of a certain attitude we extend these models by allowing players not simply to change their strategies but also let them to vary their attitudes for a higher individual income. We show that both attitudes could be the winner at a specific parameter value. Interestingly, however, the subtle interplay between different states may result in a counterintuitive evolutionary outcome where the increase of the multiplication factor of public goods game drives the population to a fully defector state. We point out that the accompanying pattern formation can only be understood via the multipoint or multi-player interactions of different microscopic states where the vicinity of a particular state may influence the relation of two other competitors.

Robot obstacle avoidance system using deep reinforcement learning

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Xiaojun Zhu ◽  
Yinghao Liang ◽  
Hanxu Sun ◽  
Xueqian Wang ◽  
Bin Ren
Keyword(s):  
Reinforcement Learning ◽  
Collision Avoidance ◽  
Obstacle Avoidance ◽  
Learning Algorithm ◽  
Optimal Path ◽  
Environmental Parameters ◽  
Working Environment ◽  
Content Type ◽  
Practical Applications ◽  
Human Operators

Purpose Most manufacturing plants choose the easy way of completely separating human operators from robots to prevent accidents, but as a result, it dramatically affects the overall quality and speed that is expected from human–robot collaboration. It is not an easy task to ensure human safety when he/she has entered a robot’s workspace, and the unstructured nature of those working environments makes it even harder. The purpose of this paper is to propose a real-time robot collision avoidance method to alleviate this problem. Design/methodology/approach In this paper, a model is trained to learn the direct control commands from the raw depth images through self-supervised reinforcement learning algorithm. To reduce the effect of sample inefficiency and safety during initial training, a virtual reality platform is used to simulate a natural working environment and generate obstacle avoidance data for training. To ensure a smooth transfer to a real robot, the automatic domain randomization technique is used to generate randomly distributed environmental parameters through the obstacle avoidance simulation of virtual robots in the virtual environment, contributing to better performance in the natural environment. Findings The method has been tested in both simulations with a real UR3 robot for several practical applications. The results of this paper indicate that the proposed approach can effectively make the robot safety-aware and learn how to divert its trajectory to avoid accidents with humans within the workspace. Research limitations/implications The method has been tested in both simulations with a real UR3 robot in several practical applications. The results indicate that the proposed approach can effectively make the robot be aware of safety and learn how to change its trajectory to avoid accidents with persons within the workspace. Originality/value This paper provides a novel collision avoidance framework that allows robots to work alongside human operators in unstructured and complex environments. The method uses end-to-end policy training to directly extract the optimal path from the visual inputs for the scene.

Non-Stationary Estimation of Joint Design Criteria With a Multivariate Conditional Extremes Approach

2016 ◽  
Author(s):  
Laks Raghupathi ◽  
David Randell ◽  
Kevin Ewans ◽  
Philip Jonathan
Keyword(s):  
Wave Height ◽  
Significant Wave Height ◽  
Structural Reliability ◽  
Structural Response ◽  
Environmental Parameters ◽  
Joint Estimation ◽  
Current Profile ◽  
Practical Applications ◽  
Significant Wave ◽  
Northern North Sea

Understanding the interaction of ocean environments with fixed and floating structures is critical to the design of offshore and coastal facilities. Structural response to environmental loading is typically the combined effect of multiple environmental parameters over a period of time. Knowledge of the tails of marginal and joint distributions of these parameters (e.g. storm peak significant wave height and associated current) as a function of covariates (e.g. dominant wave and current directions) is central to the estimation of extreme structural response, and hence of structural reliability and safety. In this paper, we present a framework for the joint estimation of multivariate extremal dependencies with multi-dimensional covariates. We demonstrate proof of principle with a synthetic bi-variate example with two covariates quantified by rigorous uncertainty analysis. We further substantiate it using two practical applications (associated current given significant wave height for northern North Sea and joint current profile for offshore Brazil locations). Further applications include the estimation of associated criteria for response-based design (e.g., TP given HS), extreme current profiles with depth for mooring and riser loading, weathervaning systems with non-stationary effects for the design of FLNG/FPSO installations, etc.

scholarly journals Understanding the Evolutionary Games in NSCLC Microenvironment

2020 ◽  
Author(s):  
Ranjini Bhattacharya ◽  
Robert Vander Velde ◽  
Viktoriya Marusyk ◽  
Bina Desai ◽  
Artem Kaznatcheev ◽  
...  
Keyword(s):  
Tumor Cells ◽  
Targeted Therapies ◽  
Evolutionary Dynamics ◽  
Fusion Gene ◽  
Evolutionary Game ◽  
Therapy Resistance ◽  
Resistance Mechanisms ◽  
Intrinsic Resistance ◽  
Frequency Changes

AbstractWhile initially highly successful, targeted therapies eventually fail as populations of tumor cells evolve mechanisms of resistance, leading to resumption of tumor growth. Historically, cell-intrinsic mutational changes have been the major focus of experimental and clinical studies to decipher origins of therapy resistance. While the importance of these mutational changes is undeniable, a growing body of evidence suggests that non-cell autonomous interactions between sub-populations of tumor cells, as well as with non-tumor cells within tumor microenvironment, might have a profound impact on both short term sensitivity of cancer cells to therapies, as well as on the evolutionary dynamics of emergent resistance. In contrast to well established tools to interrogate the functional impact of cell-intrinsic mutational changes, methodologies to understand non-cell autonomous interactions are largely lacking.Evolutionary Game Theory (EGT) is one of the main frameworks to understand the dynamics that drive frequency changes in interacting competing populations with different phenotypic strategies. However, despite a few notable exceptions, the use of EGT to understand evolutionary dynamics in the context of evolving tumors has been largely confined to theoretical studies. In order to apply EGT towards advancing our understanding of evolving tumor populations, we decided to focus on the context of the emergence of resistance to targeted therapies, directed against EML4-ALK fusion gene in lung cancers, as clinical responses to ALK inhibitors represent a poster child of limitations, posed by evolving resistance. To this end, we have examined competitive dynamics between differentially labelled therapy-naïve tumor cells, cells with cell-intrinsic resistance mechanisms, and cells with cell-extrinsic resistance, mediated by paracrine action of hepatocyte growth factor (HGF), within in vitro game assays in the presence or absence of front-line ALK inhibitor alectinib. We found that producers of HGF were the fittest in every pairwise game, while also supporting the proliferation of therapy-naïve cells. Both selective advantage of these producer cells and their impact on total population growth was a linearly increasing function of the initial frequency of producers until eventually reaching a plateau. Resistant cells did not significantly interact with the other two phenotypes. These results provide insights on reconciling selection driven emergence of subpopulations with cell non-cell autonomous resistance mechanisms, with lack of evidence of clonal dominance of these subpopulations. Further, our studies elucidate mechanisms for co-existence of multiple resistance strategies within evolving tumors. This manuscript serves as a technical report and will be followed up with a research paper in a different journal.

scholarly journals GenC: A Fast Tool for Applications Involving Belief Revision

2020 ◽  
Author(s):  
Aaron Hunter ◽  
John Agapeyev
Keyword(s):  
Theoretical Model ◽  
Parallel Processing ◽  
Incomplete Information ◽  
Belief Revision ◽  
Hamming Distance ◽  
Difference Operators ◽  
Practical Applications ◽  
Reasoning System ◽  
Extensive Class

The process of belief revision occurs in many applications where agents may have incorrect or incomplete information. One important theoretical model of belief revision is the well-known AGM approach. Unfortunately, there are few tools available for solving AGM revision problems quickly; this has limited the use of AGM operators for practical applications. In this demonstration paper, we describe GenC, a tool that is able to quickly calculate the result of AGM belief revision for formulas with hundreds of variables and millions of clauses. GenC uses an AllSAT solver and parallel processing to solve revision problems at a rate much faster than existing systems. The solver works for the class of parametrised difference operators, which is an extensive class of revision operators that use a weighted Hamming distance to measure the similarity between states. We demonstrate how GenC can be used as a stand-alone tool or as a component of a reasoning system for a variety of applications.

Study on Cooperative Evolution Behaviors in Spatial Public Goods Game Based on Self-Questioning Mechanism

2013 ◽  
Vol 380-384 ◽  
pp. 1783-1787
Author(s):  
Rui Xue Feng ◽  
Juan Ge
Keyword(s):  
Game Theory ◽  
Public Goods ◽  
Evolutionary Game Theory ◽  
Cooperative Behavior ◽  
Evolutionary Game ◽  
The Self ◽  
Square Lattice ◽  
Public Goods Game ◽  
Intensity Parameter ◽  
Cooperative Evolution

We introduce a self-questioning mechanism under spatial public goods game in the framework of Evolutionary Game Theory where players are located on a square lattice and realize it by a intensity parameter a. By stimulation and analysis, we find that compared with the original Fermi updating (a=0), the introduction of the self-questioning (a>0) can be better promote cooperative behavior at the smaller r. Subsequently, we stimulate in self-questioning mechanism (a=1), the cooperator frequency fc as a function of the factor r for different values of noise K. Results show that at the larger and smaller noise K, the system presents a considerably different cooperation phenomenon. Whats more, fc as a function of r has center symmetry nature about point (5.0, 0.5) whatever the noise K is. Further analysis indicates the reasons for the formation of these phenomena. Finally, we report the agents average payoff in the steady state and its reasons for it.

Evolutionary Game Analysis of the Government Supervision and Private Sector Ecological Technology Innovation Behavior for Water Environment Treatment PPP Projects on the basis of Public Participation

2021 ◽  
Author(s):  
Huimin Li ◽  
Fuqiang Wang ◽  
Lelin Lv ◽  
Qing Xia ◽  
Lunyan Wang
Keyword(s):  
Private Sector ◽  
Public Participation ◽  
Technology Innovation ◽  
Evolutionary Game ◽  
Water Environment ◽  
Environmental Benefits ◽  
Public And Private ◽  
Government Supervision ◽  
The Government ◽  
Innovation Behavior

Ecological technology innovation with environmental benefits as the core has become an inevitable choice for water environment treatment PPP projects (WETP-PPP), and government supervision and public participation are essential driving factors for eco-technological innovation. To explore the influence of the public participation on the behavior of government and private sector in the WETP-PPP, this study constructed an asymmetric evolutionary game model of government supervision and private sector ecological technology innovation behavior under public participation. The main contribution of this study is to explore the mutual evolutionary regularity of the private sector and government supervision department and the influence of public participation level on public and private behavior in different scenarios. The results showed that the government can reduce the supervision cost by increasing the public's active participation and improving environmental regulation measures to achieve a win-win situation of economic and environmental performance.

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