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      Balancing long-term and short-term strategies in a sustainability game

      research-article
      1 , 3 , , 2 , ∗∗
      iScience
      Elsevier
      Applied computing, Energy Modelling, Social sciences, Economics

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          Summary

          Our society is marked by a tension between short-term objectives, such as economic growth, and long-term sustainability goals, including mitigating resource depletion. In such a competitive setting, it is crucial to ascertain whether a system can maintain long-term viability and, if so, how. This article aims to enhance the understanding of this issue by analyzing how sustainability concerns change over time by means of a game, and the effect of this variation on the final status of a system. Leveraging insights from the game, we implement an agent-based model to elicit the tension between short-term objectives and sustainability, emphasizing the influence of individual actions on the overall system. The simulation results suggest that the likelihood of a collapse is contingent upon the availability of resources and the manner in which information regarding these resources is gathered and utilized. Finally, the paper proposes practical suggestions for managing this kind of system.

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          Highlights

          • Game where short-term political threats oppose long-term sustainability challenges

          • Game encoded into an agent-based model, allowing players to interact on a network

          • Results suggest collapse could happen with inability to process long-term information

          Abstract

          Applied computing; Energy Modelling; Social sciences; Economics

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          Most cited references95

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          Collective dynamics of 'small-world' networks.

          Networks of coupled dynamical systems have been used to model biological oscillators, Josephson junction arrays, excitable media, neural networks, spatial games, genetic control networks and many other self-organizing systems. Ordinarily, the connection topology is assumed to be either completely regular or completely random. But many biological, technological and social networks lie somewhere between these two extremes. Here we explore simple models of networks that can be tuned through this middle ground: regular networks 'rewired' to introduce increasing amounts of disorder. We find that these systems can be highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs. We call them 'small-world' networks, by analogy with the small-world phenomenon (popularly known as six degrees of separation. The neural network of the worm Caenorhabditis elegans, the power grid of the western United States, and the collaboration graph of film actors are shown to be small-world networks. Models of dynamical systems with small-world coupling display enhanced signal-propagation speed, computational power, and synchronizability. In particular, infectious diseases spread more easily in small-world networks than in regular lattices.
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            Statistical mechanics of complex networks

            Reviews of Modern Physics, 74(1), 47-97
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              Planetary Boundaries: Exploring the Safe Operating Space for Humanity

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                Author and article information

                Contributors
                Journal
                iScience
                iScience
                iScience
                Elsevier
                2589-0042
                27 May 2024
                21 June 2024
                27 May 2024
                : 27
                : 6
                : 110020
                Affiliations
                [1 ]School of Industrial Engineering, LIUC Università Cattaneo, Castellanza, Province of Varese, Italy
                [2 ]Centre for Study of Existential Risk, University of Cambridge, Cambridge, UK
                Author notes
                []Corresponding author fbertolotti@ 123456liuc.it
                [∗∗ ]Corresponding author sr911@ 123456cam.ac.uk
                [3]

                Lead contact

                Article
                S2589-0042(24)01245-8 110020
                10.1016/j.isci.2024.110020
                11211896
                91ac9683-46d8-44a8-ae0f-cf70353c9dd2
                © 2024 The Authors. Published by Elsevier Inc.

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

                History
                : 20 December 2023
                : 31 March 2024
                : 14 May 2024
                Categories
                Article

                applied computing,energy modelling,social sciences,economics

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