Human-environment feedback and the consistency of proenvironmental behaviour

Ecotiere, Claire Daniella; Billiard, Sylvain; André, Jean‐Baptiste; Collet, Pierre; Ferrière, Régis

doi:10.1101/2022.07.23.501219

Cited by 1 publication

(4 citation statements)

References 51 publications

(55 reference statements)

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“…The roots of Eq ( 16) also show that the number of equilibria is likely influenced by the encounter rate, κ, the payoff differential, β, the social norm threshold, n ¼ d B d A þd B , and the combination (product) of the individual sensitivity to the environment, τ, and the differential environmental impact, l B − l A . and W is a standard Brownian motion (see Section 4 in S1 Appendix [54] for mathematical detail).…”

Section: Behavior Dynamicsmentioning

confidence: 99%

“…By taking f(x, e) = x and f(x, e) = e, we obtain the deterministic part in Eq. (S1.2) in S1 Appendix [54] while f(x, e) = x 2 gives the quadratic variation in Eq. (S1.4) in S1 Appendix.…”

Section: Author Summarymentioning

confidence: 99%

“…In Section 2 in S1 Appendix [54], we explain that, assuming the population size N very large and under Assumptions (10)(11)(12)(13)(14)(15), the sequence of stochastic processes ðX…”

Section: Author Summarymentioning

confidence: 99%

“…Local stability is tested by computing the Jacobian matrix of the system. We use the Poincare ´-Bendixson theorem to check the absence of limit cycle (Th.1.8.1 in [55], see also Section 3.2 in S1 Appendix [54]). When the existence of a stable limit cycle in addition to an attractive fixed point cannot be excluded, we simulate the dynamical system for different initial conditions.…”

Section: Author Summarymentioning

confidence: 99%

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Human-environment feedback and the consistency of proenvironmental behavior

Ecotière,

Billiard,

André

et al. 2023

PLoS Comput Biol

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Addressing global environmental crises such as anthropogenic climate change requires the consistent adoption of proenvironmental behavior by a large part of a population. Here, we develop a mathematical model of a simple behavior-environment feedback loop to ask how the individual assessment of the environmental state combines with social interactions to influence the consistent adoption of proenvironmental behavior, and how this feeds back to the perceived environmental state. In this stochastic individual-based model, individuals can switch between two behaviors, ‘active’ (or actively proenvironmental) and ‘baseline’, differing in their perceived cost (higher for the active behavior) and environmental impact (lower for the active behavior). We show that the deterministic dynamics and the stochastic fluctuations of the system can be approximated by ordinary differential equations and a Ornstein-Uhlenbeck type process. By definition, the proenvironmental behavior is adopted consistently when, at population stationary state, its frequency is high and random fluctuations in frequency are small. We find that the combination of social and environmental feedbacks can promote the spread of costly proenvironmental behavior when neither, operating in isolation, would. To be adopted consistently, strong social pressure for proenvironmental action is necessary but not sufficient—social interactions must occur on a faster timescale compared to individual assessment, and the difference in environmental impact must be small. This simple model suggests a scenario to achieve large reductions in environmental impact, which involves incrementally more active and potentially more costly behavior being consistently adopted under increasing social pressure for proenvironmentalism.

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Section: Behavior Dynamicsmentioning

confidence: 99%

“…By taking f(x, e) = x and f(x, e) = e, we obtain the deterministic part in Eq. (S1.2) in S1 Appendix [54] while f(x, e) = x 2 gives the quadratic variation in Eq. (S1.4) in S1 Appendix.…”

Section: Author Summarymentioning

confidence: 99%

“…In Section 2 in S1 Appendix [54], we explain that, assuming the population size N very large and under Assumptions (10)(11)(12)(13)(14)(15), the sequence of stochastic processes ðX…”

Section: Author Summarymentioning

confidence: 99%

Section: Author Summarymentioning

confidence: 99%

See 2 more Smart Citations