Self-organisation of common good usage and an application to Internet services

TL;DR

The Win-Stay, Lose-Shift strategy can be used to understand animal dispersal on grazing and foraging land, to propose solutions to operators of systems of public transport and other technological commons, and to address problems of common good usage in social systems through decentralized governance rather than control-oriented policies.

Abstract

Natural and human-made common goods present key challenges due to their susceptibility to degradation, overuse, or congestion. We explore the self-organisation of their usage when individuals have access to several available commons but limited information on them. We propose an extension of the Win-Stay, Lose-Shift (WSLS) strategy for such systems, under which individuals use a resource iteratively until they are unsuccessful and then shift randomly. This simple strategy leads to a distribution of the use of commons with an improvement against random shifting. Selective individuals who retain information on their usage and accordingly adapt their tolerance to failure in each common good improve the average experienced quality for an entire population. Hybrid systems of selective and non-selective individuals can lead to an equilibrium with equalised experienced quality akin to the ideal free distribution. We show that these results can be applied to the server selection problem faced by mobile users accessing Internet services and we perform realistic simulations to test their validity. Furthermore, these findings can be used to understand other real systems such as animal dispersal on grazing and foraging land, and to propose solutions to operators of systems of public transport or other technological commons.

Figures (7)

• Figure 1: Representation of a system of common good usage. On the left, individuals can choose which of the $N_g$ goods they will use without any information besides their individual experience. On the right, the distribution of the population of individuals over the available goods.
• Figure 2: Server selection for Internet access as a system of common good usage. A population is constituted of active mobile users who connect to the network through a base station. They have their connection attributed through the backhaul to their chosen server $G_i$ out of $N_g$ available options.
• Figure 3: Simulation of a population of 1000 users using a WSLS strategy accessing three servers with different capacity and delay values. We show the evolution of the population distribution and server-specific failure probability for different system workload values. See "Materials and Methods" for details on the simulator and table \ref{['tab:simulator_parameters']} for the used parameters.
• Figure 4: Distribution of a population over three common goods. The orange lines represent the number of users at each server at the equalised quality equilibrium, with $n_1^{\ast}<n_2^{\ast}<n_3^{\ast}$, for a given total population size $N_u$. On the left, the non-selective equilibrium given by equation \ref{['eq:general_equilibrium']} makes $G_1$ and $G_2$ overused and $G_3$ underused. On the right, selective individuals in a hybrid population are shown in green. The system is chosen at $\gamma=\gamma^c$, i.e. the minimum proportion of selective individuals that allows the population to achieve equalised quality. Because of that, only non-selective individuals use $G_1$ in the hybrid system. At the equalised quality equilibrium, non-selective individuals use the three goods at the same rate. This is achieved through selective individuals avoiding $G_1$ and distributing over $G_2$ and $G_3$ respecting equation \ref{['eq:tolerance_hybrid_equalised']}.
• Figure 5: Simulation of a population of 1000 users using a WSLS strategy with adaptive tolerance to common goods failure accessing three servers with different capacity and delay values. We show the evolution of the population distribution, server-specific failure probability, and average tolerance under a system workload of $\rho=0.75$. Tolerance values are learned by each user independently. The value associated with "Smoothed" curve reports the average of the low-pass-filtered system-level failure probability, taken over the last 10% of samples. See "Materials and Methods" for details on the simulator and the adaptive tolerance method, and see table \ref{['tab:simulator_parameters']} for the used parameters.

Theorems & Definitions (3)

• Definition 1
• Theorem 1
• proof