Optimal Control for Sustainable Consumption of Natural Resources

Manzoor, Talha; Асеев, С.М.; Rovenskaya, E.; Muhammad, Abubakr

doi:10.3182/20140824-6-za-1003.01474

Cited by 6 publications

(5 citation statements)

References 23 publications

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“…The closed-loop system (9) however predicts the behavior of the consumers if they act rationally according to the game defined in Section III. As mentioned in Section I, this highlights the relevance of game theory to distributed control problems, whereby the selected payoffs and learning scheme may be treated as design principles in the bigger problem of social control [11], [20] in order to obtain desired behavior. Indeed as we find here, the selected scheme eliminates the phenomenon of free-riding, which poses a significant challenge in the effective governance of natural resources [17].…”

Section: Simulation Results: Overcoming Free-riding Behaviormentioning

confidence: 95%

Learning Through Fictitious Play in a Game-Theoretic Model of Natural Resource Consumption

Manzoor

Rovenskaya

Davydov

et al. 2018

IEEE Control Syst. Lett.

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Abstract-Understanding the emergence of sustainable behavior in dynamic models of resource consumption is essential for control of coupled human and natural systems. In this paper we analyze a mathematical model of resource exploitation recently reported by the authors. The model incorporates the cognitive decision-making process of consumers and has previously been studied in a game-theoretic context as a static two-player game. In this paper we extend the analysis by allowing the agents to adapt their psychological characteristics according to simple bestresponse learning dynamics. We show that, under the selected learning scheme, the Nash Equilibrium is reachable provided certain conditions on the psychological attributes of the consumers are fulfilled. Moreover, the Equilibrium solution obtained is found to be sustainable in the sense that no players exhibit free-riding behavior, a phenomenon which occurs in the original open-loop system. In the process, via a Lyapunov-function based approach, we also provide a proof for the asymptotic global stability of the original system which was previously known to be only locally stable.

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Section: Simulation Results: Overcoming Free-riding Behaviormentioning

confidence: 95%

Learning Through Fictitious Play in a Game-Theoretic Model of Natural Resource Consumption

Manzoor

Rovenskaya

Davydov

et al. 2018

IEEE Control Syst. Lett.

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“…Sustainable Development Goals [36] (SDG) have been proposed by the United Nations (UN) to describe several sustainability criteria in form of goals, tasks, and scenarios. Environment and society related sustainability goals can be broken down to numerous constraints and objectives [28,7] that affect everyday decisions, such reduction of CO 2 emission with everyday activities. Making an optimal decision with respect to such constraints and objectives often introduces potential trade-offs, e.g.…”

Section: Introductionmentioning

confidence: 99%

“…Although the effect of sustainable recommendations on individual purchases is often observed [30,2,5], there is little quantitative analysis on the collective impact of more sustainable decisions on emissions, pollution, resource usage, and personal values. Existing analytical models that often estimate such impact do not deal or represent real-world data on individual/microscopic level, but rather consists mostly of theoretical economical and climate models that evaluate sustainability on a macroscopic or societal level [28,35]. A welfare objective function that includes analytical terms representing satisfaction from consumption and environmental pollution is often optimized [31,35].…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Optimization for Value-Sensitive and Sustainable Basket Recommendations

Asikis¹

2021

Preprint

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Sustainable consumption aims to minimize the environmental and societal impact of the use of services and products. Over-consumption of services and products leads to potential natural resource exhaustion and societal inequalities, as access to goods and services becomes more challenging. In everyday life, a person can simply achieve more sustainable purchases by drastically changing their lifestyle choices and potentially going against their personal values or wishes. Conversely, achieving sustainable consumption while accounting for personal values is a more complex task, as potential trade-offs arise when trying to satisfy environmental and personal goals. This article focuses on value-sensitive design of recommender systems, which enable consumers to improve the sustainability of their purchases while respecting their personal values. Value-sensitive recommendations for sustainable consumption are formalized as a multiobjective optimization problem, where each objective represents different sustainability goals and personal values. Novel and existing multiobjective algorithms calculate solutions to this problem. The solutions are proposed as personalized sustainable basket recommendations to consumers. These recommendations are evaluated on a synthetic dataset, which comprises three established real-world datasets from relevant scientific and organizational reports. The synthetic dataset contains quantitative data on product prices, nutritional values and environmental impact metrics, such as greenhouse gas emissions and water footprint. The recommended baskets are highly similar to consumer purchased baskets and aligned with both sustainability goals and personal values relevant to health, expenditure and taste. Even when consumers would accept only a fraction of recommendations, a considerable reduction of environmental impact is observed.

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“…However, asymptotic outcomes are not considered in this static one-shot game. In a recent work [17], an infinite horizon optimal control framework was applied to a dynamical model 1 to identify conditions under which an optimal prescribed consumption rate ensures resource sustainability. However, the consumption rate is not directly manipulated by taxing, pricing, or other social control policies.…”

Section: Introductionmentioning

confidence: 99%

Optimal control policies for evolutionary dynamics with environmental feedback

Paarporn

Eksin

Weitz

et al. 2018

2018 IEEE Conference on Decision and Control (CDC)

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We study a dynamical model of a population of cooperators and defectors whose actions have long-term consequences on environmental "commons" -what we term the "resource". Cooperators contribute to restoring the resource whereas defectors degrade it. The population dynamics evolve according to a replicator equation coupled with an environmental state. Our goal is to identify methods of influencing the population with the objective to maximize accumulation of the resource. In particular, we consider strategies that modify individual-level incentives. We then extend the model to incorporate a public opinion state that imperfectly tracks the true environmental state, and study strategies that influence opinion. We formulate optimal control problems and solve them using numerical techniques to characterize locally optimal control policies for three problem formulations: 1) control of incentives, and control of opinions through 2) propaganda-like strategies and 3) awareness campaigns. We show numerically that the resulting controllers in all formulations achieve the objective, albeit with an unintended consequence. The resulting dynamics include cycles between low and high resource statesa dynamical regime termed an "oscillating tragedy of the commons". This outcome may have desirable average properties, but includes risks to resource depletion. Our findings suggest the need for new approaches to controlling coupled populationenvironment dynamics.

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Optimal Control for Sustainable Consumption of Natural Resources

Cited by 6 publications

References 23 publications

Learning Through Fictitious Play in a Game-Theoretic Model of Natural Resource Consumption

Learning Through Fictitious Play in a Game-Theoretic Model of Natural Resource Consumption

Multi-Objective Optimization for Value-Sensitive and Sustainable Basket Recommendations

Optimal control policies for evolutionary dynamics with environmental feedback

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