Sustainability is possible despite greed - Exploring the nexus between profitability and sustainability in common pool resource systems

Abstract

The sustainable use of common pool resources has become a significant global challenge. It is now widely accepted that specific mechanisms such as community-based management strategies, institutional responses such as resource privatization, information availability and emergent social norms can be used to constrain individual ‘harvesting’ to socially optimal levels. However, there is a paucity of research focused specifically on aligning profitability and sustainability goals. In this paper, an integrated mathematical model of a common pool resource game is developed to explore the nexus between the underlying costs and benefits of harvesting decisions and the sustainable level of a shared, dynamic resource. We derive optimal harvesting efforts analytically and then use numerical simulations to show that individuals in a group can learn to make harvesting decisions that lead to the globally optimal levels. Individual agents make their decision based on signals received and a trade-off between economic and ecological sustainability. When the balance is weighted towards profitability, acceptable economic and social outcomes emerge. However, if individual agents are solely driven by profit, the shared resource is depleted in the long run - sustainability is possible despite some greed, but too much will lead to over-exploitation.

Modelling coupled human–environment complexity for the future of the biosphere: strengths, gaps and promising directions

“…In a common-pool resource model, agents have their payoff reduced relative to their harvest effort by a cost per unit effort parameter, which acts similarly to a negative mitigation cost. Here, high levels lead to the persistence of the resource even when individuals are motivated by profit over sustainability goals [ 101 ].…”

The Minds of Many: Opponent Modeling in a Stochastic Game

“…Ideally, the resource N stays on a stable positive level (see the baseline optimal agent type), whereas the assets A would grow. It is difficult for agents to harvest the resource in a stable and profitable manner (only 7% of the action space A are profitable and sustainable [von der Osten et al, 2017]), even more so with increasing population, as the impact of an individual action on the overall outcome decreases. …”

“…This application of ToM shows its potential in the field of AI. Other approaches [Yoshida et al, 2008;Pereira et al, 2011] keep a model to learn beliefs about opponents' actions and a separate model to infer the complexity of opponent strategies, whereas De Weerd et al [2013b] integrate beliefs about opponent behaviour with inference about the sophistication of their strategies (see integration of different orders of ToM in Section 3). Furthermore, a game theoretic advantage of ToM is that it allows agents to infer the opponents degree of sophistication [Yoshida et al, 2008].…”

“…In the experiments in Section 4, agents either have complete knowledge (CI) of the game dynamics, or the pay-off π is not known to agents, but approximated by means of Q-Learning [Watkins and Dayan, 1992]. Agents learn their expected pay-off given other agents' contributions and their own decision in a given game state.…”

“…ToM models are particularly useful in stochastic games due to the fact that recognizing the strategies of opponents gives a player a significant advantage. The stochastic game used in the experiments is the Common Pool Resource Game (CPRG) [Sethi and Somanathan, 1996;von der Osten et al, 2017], a large-scale social dilemma. This game is particularly suitable as a demonstration for MToM, as in reality individual behaviour is dependent on beliefs about the behaviour of other individuals [Cavalcanti et al, 2010].…”